Updated Weights
Browse files- 1_Pooling/config.json +10 -0
- README.md +491 -0
- checkpoint-2240/1_Pooling/config.json +10 -0
- checkpoint-2240/README.md +500 -0
- checkpoint-2240/config.json +63 -0
- checkpoint-2240/config_sentence_transformers.json +10 -0
- checkpoint-2240/model.safetensors +3 -0
- checkpoint-2240/modules.json +14 -0
- checkpoint-2240/optimizer.pt +3 -0
- checkpoint-2240/rng_state.pth +3 -0
- checkpoint-2240/scheduler.pt +3 -0
- checkpoint-2240/sentence_bert_config.json +4 -0
- checkpoint-2240/special_tokens_map.json +125 -0
- checkpoint-2240/tokenizer.json +0 -0
- checkpoint-2240/tokenizer_config.json +939 -0
- checkpoint-2240/trainer_state.json +150 -0
- checkpoint-2240/training_args.bin +3 -0
- config.json +63 -0
- config_sentence_transformers.json +10 -0
- eval/Information-Retrieval_evaluation_test-eval_results.csv +284 -0
- model.safetensors +3 -0
- modules.json +14 -0
- sentence_bert_config.json +4 -0
- special_tokens_map.json +125 -0
- tokenizer.json +0 -0
- tokenizer_config.json +939 -0
1_Pooling/config.json
ADDED
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{
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"word_embedding_dimension": 768,
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"pooling_mode_cls_token": false,
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"pooling_mode_mean_tokens": true,
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"pooling_mode_max_tokens": false,
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"pooling_mode_mean_sqrt_len_tokens": false,
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"pooling_mode_weightedmean_tokens": false,
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"pooling_mode_lasttoken": false,
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"include_prompt": true
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}
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README.md
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|
| 1 |
+
---
|
| 2 |
+
tags:
|
| 3 |
+
- sentence-transformers
|
| 4 |
+
- sentence-similarity
|
| 5 |
+
- feature-extraction
|
| 6 |
+
- generated_from_trainer
|
| 7 |
+
- dataset_size:2620
|
| 8 |
+
- loss:MultipleNegativesRankingLoss
|
| 9 |
+
- loss:CosineSimilarityLoss
|
| 10 |
+
base_model: jinaai/jina-embedding-b-en-v1
|
| 11 |
+
widget:
|
| 12 |
+
- source_sentence: What sector am I most heavily invested in?
|
| 13 |
+
sentences:
|
| 14 |
+
- 'Show me how to switch my stock portfolio to mutual funds
|
| 15 |
+
|
| 16 |
+
'
|
| 17 |
+
- What percentage of my portfolio is in X
|
| 18 |
+
- Which sector do I invest most in?
|
| 19 |
+
- source_sentence: Can you tell me how my portfolio ranks among others?
|
| 20 |
+
sentences:
|
| 21 |
+
- What is my AMC wise split ?
|
| 22 |
+
- In which funds am I paying highest fees
|
| 23 |
+
- Compare my portfolio with others?
|
| 24 |
+
- source_sentence: Which of my funds has the highest risk level?
|
| 25 |
+
sentences:
|
| 26 |
+
- Give me python code to find best funds in my portfolio
|
| 27 |
+
- Show my stocks ranked by performance
|
| 28 |
+
- Show my riskiest mutual funds
|
| 29 |
+
- source_sentence: What's going right with my portfolio?
|
| 30 |
+
sentences:
|
| 31 |
+
- Is my portfolio linked?
|
| 32 |
+
- My portfolio returns over all the years
|
| 33 |
+
- What's going well in my portfolio
|
| 34 |
+
- source_sentence: I'd like to know the percentage of large cap in my investments.
|
| 35 |
+
sentences:
|
| 36 |
+
- Show my riskiest holdings
|
| 37 |
+
- Can you show what percentage of my portfolio consists of large cap
|
| 38 |
+
- What is the expected return of my portfolio?
|
| 39 |
+
pipeline_tag: sentence-similarity
|
| 40 |
+
library_name: sentence-transformers
|
| 41 |
+
metrics:
|
| 42 |
+
- cosine_accuracy@1
|
| 43 |
+
- cosine_accuracy@3
|
| 44 |
+
- cosine_accuracy@5
|
| 45 |
+
- cosine_accuracy@10
|
| 46 |
+
- cosine_precision@1
|
| 47 |
+
- cosine_precision@3
|
| 48 |
+
- cosine_precision@5
|
| 49 |
+
- cosine_precision@10
|
| 50 |
+
- cosine_recall@1
|
| 51 |
+
- cosine_recall@3
|
| 52 |
+
- cosine_recall@5
|
| 53 |
+
- cosine_recall@10
|
| 54 |
+
- cosine_ndcg@10
|
| 55 |
+
- cosine_mrr@10
|
| 56 |
+
- cosine_map@100
|
| 57 |
+
model-index:
|
| 58 |
+
- name: SentenceTransformer based on jinaai/jina-embedding-b-en-v1
|
| 59 |
+
results:
|
| 60 |
+
- task:
|
| 61 |
+
type: information-retrieval
|
| 62 |
+
name: Information Retrieval
|
| 63 |
+
dataset:
|
| 64 |
+
name: test eval
|
| 65 |
+
type: test-eval
|
| 66 |
+
metrics:
|
| 67 |
+
- type: cosine_accuracy@1
|
| 68 |
+
value: 0.8625954198473282
|
| 69 |
+
name: Cosine Accuracy@1
|
| 70 |
+
- type: cosine_accuracy@3
|
| 71 |
+
value: 0.9961832061068703
|
| 72 |
+
name: Cosine Accuracy@3
|
| 73 |
+
- type: cosine_accuracy@5
|
| 74 |
+
value: 1.0
|
| 75 |
+
name: Cosine Accuracy@5
|
| 76 |
+
- type: cosine_accuracy@10
|
| 77 |
+
value: 1.0
|
| 78 |
+
name: Cosine Accuracy@10
|
| 79 |
+
- type: cosine_precision@1
|
| 80 |
+
value: 0.8625954198473282
|
| 81 |
+
name: Cosine Precision@1
|
| 82 |
+
- type: cosine_precision@3
|
| 83 |
+
value: 0.33206106870229
|
| 84 |
+
name: Cosine Precision@3
|
| 85 |
+
- type: cosine_precision@5
|
| 86 |
+
value: 0.19999999999999998
|
| 87 |
+
name: Cosine Precision@5
|
| 88 |
+
- type: cosine_precision@10
|
| 89 |
+
value: 0.09999999999999999
|
| 90 |
+
name: Cosine Precision@10
|
| 91 |
+
- type: cosine_recall@1
|
| 92 |
+
value: 0.8625954198473282
|
| 93 |
+
name: Cosine Recall@1
|
| 94 |
+
- type: cosine_recall@3
|
| 95 |
+
value: 0.9961832061068703
|
| 96 |
+
name: Cosine Recall@3
|
| 97 |
+
- type: cosine_recall@5
|
| 98 |
+
value: 1.0
|
| 99 |
+
name: Cosine Recall@5
|
| 100 |
+
- type: cosine_recall@10
|
| 101 |
+
value: 1.0
|
| 102 |
+
name: Cosine Recall@10
|
| 103 |
+
- type: cosine_ndcg@10
|
| 104 |
+
value: 0.9460250731496836
|
| 105 |
+
name: Cosine Ndcg@10
|
| 106 |
+
- type: cosine_mrr@10
|
| 107 |
+
value: 0.9271628498727736
|
| 108 |
+
name: Cosine Mrr@10
|
| 109 |
+
- type: cosine_map@100
|
| 110 |
+
value: 0.9271628498727736
|
| 111 |
+
name: Cosine Map@100
|
| 112 |
+
---
|
| 113 |
+
|
| 114 |
+
# SentenceTransformer based on jinaai/jina-embedding-b-en-v1
|
| 115 |
+
|
| 116 |
+
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [jinaai/jina-embedding-b-en-v1](https://huggingface.co/jinaai/jina-embedding-b-en-v1). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
|
| 117 |
+
|
| 118 |
+
## Model Details
|
| 119 |
+
|
| 120 |
+
### Model Description
|
| 121 |
+
- **Model Type:** Sentence Transformer
|
| 122 |
+
- **Base model:** [jinaai/jina-embedding-b-en-v1](https://huggingface.co/jinaai/jina-embedding-b-en-v1) <!-- at revision 32aa658e5ceb90793454d22a57d8e3a14e699516 -->
|
| 123 |
+
- **Maximum Sequence Length:** 512 tokens
|
| 124 |
+
- **Output Dimensionality:** 768 dimensions
|
| 125 |
+
- **Similarity Function:** Cosine Similarity
|
| 126 |
+
<!-- - **Training Dataset:** Unknown -->
|
| 127 |
+
<!-- - **Language:** Unknown -->
|
| 128 |
+
<!-- - **License:** Unknown -->
|
| 129 |
+
|
| 130 |
+
### Model Sources
|
| 131 |
+
|
| 132 |
+
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
|
| 133 |
+
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
|
| 134 |
+
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
|
| 135 |
+
|
| 136 |
+
### Full Model Architecture
|
| 137 |
+
|
| 138 |
+
```
|
| 139 |
+
SentenceTransformer(
|
| 140 |
+
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: T5EncoderModel
|
| 141 |
+
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
|
| 142 |
+
)
|
| 143 |
+
```
|
| 144 |
+
|
| 145 |
+
## Usage
|
| 146 |
+
|
| 147 |
+
### Direct Usage (Sentence Transformers)
|
| 148 |
+
|
| 149 |
+
First install the Sentence Transformers library:
|
| 150 |
+
|
| 151 |
+
```bash
|
| 152 |
+
pip install -U sentence-transformers
|
| 153 |
+
```
|
| 154 |
+
|
| 155 |
+
Then you can load this model and run inference.
|
| 156 |
+
```python
|
| 157 |
+
from sentence_transformers import SentenceTransformer
|
| 158 |
+
|
| 159 |
+
# Download from the 🤗 Hub
|
| 160 |
+
model = SentenceTransformer("sentence_transformers_model_id")
|
| 161 |
+
# Run inference
|
| 162 |
+
sentences = [
|
| 163 |
+
"I'd like to know the percentage of large cap in my investments.",
|
| 164 |
+
'Can you show what percentage of my portfolio consists of large cap',
|
| 165 |
+
'Show my riskiest holdings',
|
| 166 |
+
]
|
| 167 |
+
embeddings = model.encode(sentences)
|
| 168 |
+
print(embeddings.shape)
|
| 169 |
+
# [3, 768]
|
| 170 |
+
|
| 171 |
+
# Get the similarity scores for the embeddings
|
| 172 |
+
similarities = model.similarity(embeddings, embeddings)
|
| 173 |
+
print(similarities.shape)
|
| 174 |
+
# [3, 3]
|
| 175 |
+
```
|
| 176 |
+
|
| 177 |
+
<!--
|
| 178 |
+
### Direct Usage (Transformers)
|
| 179 |
+
|
| 180 |
+
<details><summary>Click to see the direct usage in Transformers</summary>
|
| 181 |
+
|
| 182 |
+
</details>
|
| 183 |
+
-->
|
| 184 |
+
|
| 185 |
+
<!--
|
| 186 |
+
### Downstream Usage (Sentence Transformers)
|
| 187 |
+
|
| 188 |
+
You can finetune this model on your own dataset.
|
| 189 |
+
|
| 190 |
+
<details><summary>Click to expand</summary>
|
| 191 |
+
|
| 192 |
+
</details>
|
| 193 |
+
-->
|
| 194 |
+
|
| 195 |
+
<!--
|
| 196 |
+
### Out-of-Scope Use
|
| 197 |
+
|
| 198 |
+
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
|
| 199 |
+
-->
|
| 200 |
+
|
| 201 |
+
## Evaluation
|
| 202 |
+
|
| 203 |
+
### Metrics
|
| 204 |
+
|
| 205 |
+
#### Information Retrieval
|
| 206 |
+
|
| 207 |
+
* Dataset: `test-eval`
|
| 208 |
+
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
|
| 209 |
+
|
| 210 |
+
| Metric | Value |
|
| 211 |
+
|:--------------------|:----------|
|
| 212 |
+
| cosine_accuracy@1 | 0.8626 |
|
| 213 |
+
| cosine_accuracy@3 | 0.9962 |
|
| 214 |
+
| cosine_accuracy@5 | 1.0 |
|
| 215 |
+
| cosine_accuracy@10 | 1.0 |
|
| 216 |
+
| cosine_precision@1 | 0.8626 |
|
| 217 |
+
| cosine_precision@3 | 0.3321 |
|
| 218 |
+
| cosine_precision@5 | 0.2 |
|
| 219 |
+
| cosine_precision@10 | 0.1 |
|
| 220 |
+
| cosine_recall@1 | 0.8626 |
|
| 221 |
+
| cosine_recall@3 | 0.9962 |
|
| 222 |
+
| cosine_recall@5 | 1.0 |
|
| 223 |
+
| cosine_recall@10 | 1.0 |
|
| 224 |
+
| **cosine_ndcg@10** | **0.946** |
|
| 225 |
+
| cosine_mrr@10 | 0.9272 |
|
| 226 |
+
| cosine_map@100 | 0.9272 |
|
| 227 |
+
|
| 228 |
+
<!--
|
| 229 |
+
## Bias, Risks and Limitations
|
| 230 |
+
|
| 231 |
+
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
|
| 232 |
+
-->
|
| 233 |
+
|
| 234 |
+
<!--
|
| 235 |
+
### Recommendations
|
| 236 |
+
|
| 237 |
+
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
|
| 238 |
+
-->
|
| 239 |
+
|
| 240 |
+
## Training Details
|
| 241 |
+
|
| 242 |
+
### Training Datasets
|
| 243 |
+
|
| 244 |
+
#### Unnamed Dataset
|
| 245 |
+
|
| 246 |
+
* Size: 1,310 training samples
|
| 247 |
+
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
|
| 248 |
+
* Approximate statistics based on the first 1000 samples:
|
| 249 |
+
| | sentence_0 | sentence_1 | label |
|
| 250 |
+
|:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:--------------------------------------------------------------|
|
| 251 |
+
| type | string | string | float |
|
| 252 |
+
| details | <ul><li>min: 4 tokens</li><li>mean: 10.62 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.06 tokens</li><li>max: 17 tokens</li></ul> | <ul><li>min: 1.0</li><li>mean: 1.0</li><li>max: 1.0</li></ul> |
|
| 253 |
+
* Samples:
|
| 254 |
+
| sentence_0 | sentence_1 | label |
|
| 255 |
+
|:--------------------------------------------------------------------|:-------------------------------------------------------------------|:-----------------|
|
| 256 |
+
| <code>are there any of my funds that are lagging behind</code> | <code>do I hold any funds that haven't been performing well</code> | <code>1.0</code> |
|
| 257 |
+
| <code>Which sectors are performing the best in my portfolio?</code> | <code>What are my best performing sectors?</code> | <code>1.0</code> |
|
| 258 |
+
| <code>List some of my top holdings</code> | <code>Show some of my best performing holdings</code> | <code>1.0</code> |
|
| 259 |
+
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
|
| 260 |
+
```json
|
| 261 |
+
{
|
| 262 |
+
"scale": 20.0,
|
| 263 |
+
"similarity_fct": "cos_sim"
|
| 264 |
+
}
|
| 265 |
+
```
|
| 266 |
+
|
| 267 |
+
#### Unnamed Dataset
|
| 268 |
+
|
| 269 |
+
* Size: 1,310 training samples
|
| 270 |
+
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
|
| 271 |
+
* Approximate statistics based on the first 1000 samples:
|
| 272 |
+
| | sentence_0 | sentence_1 | label |
|
| 273 |
+
|:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:--------------------------------------------------------------|
|
| 274 |
+
| type | string | string | float |
|
| 275 |
+
| details | <ul><li>min: 4 tokens</li><li>mean: 10.68 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.13 tokens</li><li>max: 17 tokens</li></ul> | <ul><li>min: 1.0</li><li>mean: 1.0</li><li>max: 1.0</li></ul> |
|
| 276 |
+
* Samples:
|
| 277 |
+
| sentence_0 | sentence_1 | label |
|
| 278 |
+
|:--------------------------------------------------------------------|:----------------------------------------------------------|:-----------------|
|
| 279 |
+
| <code>I need my portfolio to hit 1000% returns by next month</code> | <code>make my portfolio return 1000% by next month</code> | <code>1.0</code> |
|
| 280 |
+
| <code>What are my stocks?</code> | <code>Show my stocks</code> | <code>1.0</code> |
|
| 281 |
+
| <code>I'd like to know my sector distribution.</code> | <code>What is my sector allocation?</code> | <code>1.0</code> |
|
| 282 |
+
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
|
| 283 |
+
```json
|
| 284 |
+
{
|
| 285 |
+
"loss_fct": "torch.nn.modules.loss.MSELoss"
|
| 286 |
+
}
|
| 287 |
+
```
|
| 288 |
+
|
| 289 |
+
### Training Hyperparameters
|
| 290 |
+
#### Non-Default Hyperparameters
|
| 291 |
+
|
| 292 |
+
- `eval_strategy`: steps
|
| 293 |
+
- `per_device_train_batch_size`: 32
|
| 294 |
+
- `per_device_eval_batch_size`: 32
|
| 295 |
+
- `num_train_epochs`: 15
|
| 296 |
+
- `multi_dataset_batch_sampler`: round_robin
|
| 297 |
+
|
| 298 |
+
#### All Hyperparameters
|
| 299 |
+
<details><summary>Click to expand</summary>
|
| 300 |
+
|
| 301 |
+
- `overwrite_output_dir`: False
|
| 302 |
+
- `do_predict`: False
|
| 303 |
+
- `eval_strategy`: steps
|
| 304 |
+
- `prediction_loss_only`: True
|
| 305 |
+
- `per_device_train_batch_size`: 32
|
| 306 |
+
- `per_device_eval_batch_size`: 32
|
| 307 |
+
- `per_gpu_train_batch_size`: None
|
| 308 |
+
- `per_gpu_eval_batch_size`: None
|
| 309 |
+
- `gradient_accumulation_steps`: 1
|
| 310 |
+
- `eval_accumulation_steps`: None
|
| 311 |
+
- `torch_empty_cache_steps`: None
|
| 312 |
+
- `learning_rate`: 5e-05
|
| 313 |
+
- `weight_decay`: 0.0
|
| 314 |
+
- `adam_beta1`: 0.9
|
| 315 |
+
- `adam_beta2`: 0.999
|
| 316 |
+
- `adam_epsilon`: 1e-08
|
| 317 |
+
- `max_grad_norm`: 1
|
| 318 |
+
- `num_train_epochs`: 15
|
| 319 |
+
- `max_steps`: -1
|
| 320 |
+
- `lr_scheduler_type`: linear
|
| 321 |
+
- `lr_scheduler_kwargs`: {}
|
| 322 |
+
- `warmup_ratio`: 0.0
|
| 323 |
+
- `warmup_steps`: 0
|
| 324 |
+
- `log_level`: passive
|
| 325 |
+
- `log_level_replica`: warning
|
| 326 |
+
- `log_on_each_node`: True
|
| 327 |
+
- `logging_nan_inf_filter`: True
|
| 328 |
+
- `save_safetensors`: True
|
| 329 |
+
- `save_on_each_node`: False
|
| 330 |
+
- `save_only_model`: False
|
| 331 |
+
- `restore_callback_states_from_checkpoint`: False
|
| 332 |
+
- `no_cuda`: False
|
| 333 |
+
- `use_cpu`: False
|
| 334 |
+
- `use_mps_device`: False
|
| 335 |
+
- `seed`: 42
|
| 336 |
+
- `data_seed`: None
|
| 337 |
+
- `jit_mode_eval`: False
|
| 338 |
+
- `use_ipex`: False
|
| 339 |
+
- `bf16`: False
|
| 340 |
+
- `fp16`: False
|
| 341 |
+
- `fp16_opt_level`: O1
|
| 342 |
+
- `half_precision_backend`: auto
|
| 343 |
+
- `bf16_full_eval`: False
|
| 344 |
+
- `fp16_full_eval`: False
|
| 345 |
+
- `tf32`: None
|
| 346 |
+
- `local_rank`: 0
|
| 347 |
+
- `ddp_backend`: None
|
| 348 |
+
- `tpu_num_cores`: None
|
| 349 |
+
- `tpu_metrics_debug`: False
|
| 350 |
+
- `debug`: []
|
| 351 |
+
- `dataloader_drop_last`: False
|
| 352 |
+
- `dataloader_num_workers`: 0
|
| 353 |
+
- `dataloader_prefetch_factor`: None
|
| 354 |
+
- `past_index`: -1
|
| 355 |
+
- `disable_tqdm`: False
|
| 356 |
+
- `remove_unused_columns`: True
|
| 357 |
+
- `label_names`: None
|
| 358 |
+
- `load_best_model_at_end`: False
|
| 359 |
+
- `ignore_data_skip`: False
|
| 360 |
+
- `fsdp`: []
|
| 361 |
+
- `fsdp_min_num_params`: 0
|
| 362 |
+
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
|
| 363 |
+
- `tp_size`: 0
|
| 364 |
+
- `fsdp_transformer_layer_cls_to_wrap`: None
|
| 365 |
+
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
|
| 366 |
+
- `deepspeed`: None
|
| 367 |
+
- `label_smoothing_factor`: 0.0
|
| 368 |
+
- `optim`: adamw_torch
|
| 369 |
+
- `optim_args`: None
|
| 370 |
+
- `adafactor`: False
|
| 371 |
+
- `group_by_length`: False
|
| 372 |
+
- `length_column_name`: length
|
| 373 |
+
- `ddp_find_unused_parameters`: None
|
| 374 |
+
- `ddp_bucket_cap_mb`: None
|
| 375 |
+
- `ddp_broadcast_buffers`: False
|
| 376 |
+
- `dataloader_pin_memory`: True
|
| 377 |
+
- `dataloader_persistent_workers`: False
|
| 378 |
+
- `skip_memory_metrics`: True
|
| 379 |
+
- `use_legacy_prediction_loop`: False
|
| 380 |
+
- `push_to_hub`: False
|
| 381 |
+
- `resume_from_checkpoint`: None
|
| 382 |
+
- `hub_model_id`: None
|
| 383 |
+
- `hub_strategy`: every_save
|
| 384 |
+
- `hub_private_repo`: None
|
| 385 |
+
- `hub_always_push`: False
|
| 386 |
+
- `gradient_checkpointing`: False
|
| 387 |
+
- `gradient_checkpointing_kwargs`: None
|
| 388 |
+
- `include_inputs_for_metrics`: False
|
| 389 |
+
- `include_for_metrics`: []
|
| 390 |
+
- `eval_do_concat_batches`: True
|
| 391 |
+
- `fp16_backend`: auto
|
| 392 |
+
- `push_to_hub_model_id`: None
|
| 393 |
+
- `push_to_hub_organization`: None
|
| 394 |
+
- `mp_parameters`:
|
| 395 |
+
- `auto_find_batch_size`: False
|
| 396 |
+
- `full_determinism`: False
|
| 397 |
+
- `torchdynamo`: None
|
| 398 |
+
- `ray_scope`: last
|
| 399 |
+
- `ddp_timeout`: 1800
|
| 400 |
+
- `torch_compile`: False
|
| 401 |
+
- `torch_compile_backend`: None
|
| 402 |
+
- `torch_compile_mode`: None
|
| 403 |
+
- `include_tokens_per_second`: False
|
| 404 |
+
- `include_num_input_tokens_seen`: False
|
| 405 |
+
- `neftune_noise_alpha`: None
|
| 406 |
+
- `optim_target_modules`: None
|
| 407 |
+
- `batch_eval_metrics`: False
|
| 408 |
+
- `eval_on_start`: False
|
| 409 |
+
- `use_liger_kernel`: False
|
| 410 |
+
- `eval_use_gather_object`: False
|
| 411 |
+
- `average_tokens_across_devices`: False
|
| 412 |
+
- `prompts`: None
|
| 413 |
+
- `batch_sampler`: batch_sampler
|
| 414 |
+
- `multi_dataset_batch_sampler`: round_robin
|
| 415 |
+
|
| 416 |
+
</details>
|
| 417 |
+
|
| 418 |
+
### Training Logs
|
| 419 |
+
| Epoch | Step | Training Loss | test-eval_cosine_ndcg@10 |
|
| 420 |
+
|:-------:|:----:|:-------------:|:------------------------:|
|
| 421 |
+
| 1.0 | 82 | - | 0.8929 |
|
| 422 |
+
| 2.0 | 164 | - | 0.9007 |
|
| 423 |
+
| 3.0 | 246 | - | 0.9112 |
|
| 424 |
+
| 4.0 | 328 | - | 0.9188 |
|
| 425 |
+
| 5.0 | 410 | - | 0.9285 |
|
| 426 |
+
| 6.0 | 492 | - | 0.9286 |
|
| 427 |
+
| 6.0976 | 500 | 0.2352 | 0.9291 |
|
| 428 |
+
| 7.0 | 574 | - | 0.9356 |
|
| 429 |
+
| 8.0 | 656 | - | 0.9404 |
|
| 430 |
+
| 9.0 | 738 | - | 0.9406 |
|
| 431 |
+
| 10.0 | 820 | - | 0.9434 |
|
| 432 |
+
| 11.0 | 902 | - | 0.9424 |
|
| 433 |
+
| 12.0 | 984 | - | 0.9455 |
|
| 434 |
+
| 12.1951 | 1000 | 0.164 | 0.9460 |
|
| 435 |
+
|
| 436 |
+
|
| 437 |
+
### Framework Versions
|
| 438 |
+
- Python: 3.10.16
|
| 439 |
+
- Sentence Transformers: 4.1.0
|
| 440 |
+
- Transformers: 4.51.3
|
| 441 |
+
- PyTorch: 2.7.0
|
| 442 |
+
- Accelerate: 1.6.0
|
| 443 |
+
- Datasets: 3.5.0
|
| 444 |
+
- Tokenizers: 0.21.1
|
| 445 |
+
|
| 446 |
+
## Citation
|
| 447 |
+
|
| 448 |
+
### BibTeX
|
| 449 |
+
|
| 450 |
+
#### Sentence Transformers
|
| 451 |
+
```bibtex
|
| 452 |
+
@inproceedings{reimers-2019-sentence-bert,
|
| 453 |
+
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
|
| 454 |
+
author = "Reimers, Nils and Gurevych, Iryna",
|
| 455 |
+
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
|
| 456 |
+
month = "11",
|
| 457 |
+
year = "2019",
|
| 458 |
+
publisher = "Association for Computational Linguistics",
|
| 459 |
+
url = "https://arxiv.org/abs/1908.10084",
|
| 460 |
+
}
|
| 461 |
+
```
|
| 462 |
+
|
| 463 |
+
#### MultipleNegativesRankingLoss
|
| 464 |
+
```bibtex
|
| 465 |
+
@misc{henderson2017efficient,
|
| 466 |
+
title={Efficient Natural Language Response Suggestion for Smart Reply},
|
| 467 |
+
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
|
| 468 |
+
year={2017},
|
| 469 |
+
eprint={1705.00652},
|
| 470 |
+
archivePrefix={arXiv},
|
| 471 |
+
primaryClass={cs.CL}
|
| 472 |
+
}
|
| 473 |
+
```
|
| 474 |
+
|
| 475 |
+
<!--
|
| 476 |
+
## Glossary
|
| 477 |
+
|
| 478 |
+
*Clearly define terms in order to be accessible across audiences.*
|
| 479 |
+
-->
|
| 480 |
+
|
| 481 |
+
<!--
|
| 482 |
+
## Model Card Authors
|
| 483 |
+
|
| 484 |
+
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
|
| 485 |
+
-->
|
| 486 |
+
|
| 487 |
+
<!--
|
| 488 |
+
## Model Card Contact
|
| 489 |
+
|
| 490 |
+
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
|
| 491 |
+
-->
|
checkpoint-2240/1_Pooling/config.json
ADDED
|
@@ -0,0 +1,10 @@
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| 1 |
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{
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| 2 |
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"word_embedding_dimension": 768,
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| 3 |
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"pooling_mode_cls_token": false,
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"pooling_mode_mean_tokens": true,
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| 5 |
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"pooling_mode_max_tokens": false,
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| 6 |
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"pooling_mode_mean_sqrt_len_tokens": false,
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| 7 |
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"pooling_mode_weightedmean_tokens": false,
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| 8 |
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"pooling_mode_lasttoken": false,
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| 9 |
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"include_prompt": true
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| 10 |
+
}
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checkpoint-2240/README.md
ADDED
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@@ -0,0 +1,500 @@
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| 1 |
+
---
|
| 2 |
+
tags:
|
| 3 |
+
- sentence-transformers
|
| 4 |
+
- sentence-similarity
|
| 5 |
+
- feature-extraction
|
| 6 |
+
- generated_from_trainer
|
| 7 |
+
- dataset_size:3570
|
| 8 |
+
- loss:MultipleNegativesRankingLoss
|
| 9 |
+
- loss:CosineSimilarityLoss
|
| 10 |
+
base_model: jinaai/jina-embedding-b-en-v1
|
| 11 |
+
widget:
|
| 12 |
+
- source_sentence: How do I change my stocks to mutual funds?
|
| 13 |
+
sentences:
|
| 14 |
+
- How can I swap my stocks for mutual funds?
|
| 15 |
+
- Show my stocks
|
| 16 |
+
- What are the profits I have gained in my portfolio
|
| 17 |
+
- source_sentence: What percentage of my investments are in large cap?
|
| 18 |
+
sentences:
|
| 19 |
+
- Show some of my best performing holdings
|
| 20 |
+
- Suggest recommendations for me
|
| 21 |
+
- Can you show what percentage of my portfolio consists of large cap
|
| 22 |
+
- source_sentence: How do I change my risk profile?
|
| 23 |
+
sentences:
|
| 24 |
+
- What can I do to bring down the volatility in my portfolio?
|
| 25 |
+
- I want to change my risk profile
|
| 26 |
+
- What is the total value of my portfolio
|
| 27 |
+
- source_sentence: Is now a good time to buy energy stocks considering the war in
|
| 28 |
+
the Middle East and rising fuel prices?
|
| 29 |
+
sentences:
|
| 30 |
+
- Am I investing in the small cap market more?
|
| 31 |
+
- I saw in the news that there is a war going on in the Middle East and fuel will
|
| 32 |
+
be more costly now, should I buy energy sector stocks?
|
| 33 |
+
- Are my ETFs giving better returns compare to my mutual funds?
|
| 34 |
+
- source_sentence: Look for funds that fit my stock holdings
|
| 35 |
+
sentences:
|
| 36 |
+
- Can you tell me if my investments will grow well in the long run?
|
| 37 |
+
- Do I have any stocks in my portfolio?
|
| 38 |
+
- Explore funds that match my stock portfolio
|
| 39 |
+
pipeline_tag: sentence-similarity
|
| 40 |
+
library_name: sentence-transformers
|
| 41 |
+
metrics:
|
| 42 |
+
- cosine_accuracy@1
|
| 43 |
+
- cosine_accuracy@3
|
| 44 |
+
- cosine_accuracy@5
|
| 45 |
+
- cosine_accuracy@10
|
| 46 |
+
- cosine_precision@1
|
| 47 |
+
- cosine_precision@3
|
| 48 |
+
- cosine_precision@5
|
| 49 |
+
- cosine_precision@10
|
| 50 |
+
- cosine_recall@1
|
| 51 |
+
- cosine_recall@3
|
| 52 |
+
- cosine_recall@5
|
| 53 |
+
- cosine_recall@10
|
| 54 |
+
- cosine_ndcg@10
|
| 55 |
+
- cosine_mrr@10
|
| 56 |
+
- cosine_map@100
|
| 57 |
+
model-index:
|
| 58 |
+
- name: SentenceTransformer based on jinaai/jina-embedding-b-en-v1
|
| 59 |
+
results:
|
| 60 |
+
- task:
|
| 61 |
+
type: information-retrieval
|
| 62 |
+
name: Information Retrieval
|
| 63 |
+
dataset:
|
| 64 |
+
name: test eval
|
| 65 |
+
type: test-eval
|
| 66 |
+
metrics:
|
| 67 |
+
- type: cosine_accuracy@1
|
| 68 |
+
value: 0.8659217877094972
|
| 69 |
+
name: Cosine Accuracy@1
|
| 70 |
+
- type: cosine_accuracy@3
|
| 71 |
+
value: 0.9916201117318436
|
| 72 |
+
name: Cosine Accuracy@3
|
| 73 |
+
- type: cosine_accuracy@5
|
| 74 |
+
value: 0.9972067039106145
|
| 75 |
+
name: Cosine Accuracy@5
|
| 76 |
+
- type: cosine_accuracy@10
|
| 77 |
+
value: 1.0
|
| 78 |
+
name: Cosine Accuracy@10
|
| 79 |
+
- type: cosine_precision@1
|
| 80 |
+
value: 0.8659217877094972
|
| 81 |
+
name: Cosine Precision@1
|
| 82 |
+
- type: cosine_precision@3
|
| 83 |
+
value: 0.33054003724394787
|
| 84 |
+
name: Cosine Precision@3
|
| 85 |
+
- type: cosine_precision@5
|
| 86 |
+
value: 0.1994413407821229
|
| 87 |
+
name: Cosine Precision@5
|
| 88 |
+
- type: cosine_precision@10
|
| 89 |
+
value: 0.09999999999999999
|
| 90 |
+
name: Cosine Precision@10
|
| 91 |
+
- type: cosine_recall@1
|
| 92 |
+
value: 0.8659217877094972
|
| 93 |
+
name: Cosine Recall@1
|
| 94 |
+
- type: cosine_recall@3
|
| 95 |
+
value: 0.9916201117318436
|
| 96 |
+
name: Cosine Recall@3
|
| 97 |
+
- type: cosine_recall@5
|
| 98 |
+
value: 0.9972067039106145
|
| 99 |
+
name: Cosine Recall@5
|
| 100 |
+
- type: cosine_recall@10
|
| 101 |
+
value: 1.0
|
| 102 |
+
name: Cosine Recall@10
|
| 103 |
+
- type: cosine_ndcg@10
|
| 104 |
+
value: 0.9460695277624867
|
| 105 |
+
name: Cosine Ndcg@10
|
| 106 |
+
- type: cosine_mrr@10
|
| 107 |
+
value: 0.9273743016759775
|
| 108 |
+
name: Cosine Mrr@10
|
| 109 |
+
- type: cosine_map@100
|
| 110 |
+
value: 0.9273743016759777
|
| 111 |
+
name: Cosine Map@100
|
| 112 |
+
---
|
| 113 |
+
|
| 114 |
+
# SentenceTransformer based on jinaai/jina-embedding-b-en-v1
|
| 115 |
+
|
| 116 |
+
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [jinaai/jina-embedding-b-en-v1](https://huggingface.co/jinaai/jina-embedding-b-en-v1). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
|
| 117 |
+
|
| 118 |
+
## Model Details
|
| 119 |
+
|
| 120 |
+
### Model Description
|
| 121 |
+
- **Model Type:** Sentence Transformer
|
| 122 |
+
- **Base model:** [jinaai/jina-embedding-b-en-v1](https://huggingface.co/jinaai/jina-embedding-b-en-v1) <!-- at revision 32aa658e5ceb90793454d22a57d8e3a14e699516 -->
|
| 123 |
+
- **Maximum Sequence Length:** 512 tokens
|
| 124 |
+
- **Output Dimensionality:** 768 dimensions
|
| 125 |
+
- **Similarity Function:** Cosine Similarity
|
| 126 |
+
<!-- - **Training Dataset:** Unknown -->
|
| 127 |
+
<!-- - **Language:** Unknown -->
|
| 128 |
+
<!-- - **License:** Unknown -->
|
| 129 |
+
|
| 130 |
+
### Model Sources
|
| 131 |
+
|
| 132 |
+
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
|
| 133 |
+
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
|
| 134 |
+
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
|
| 135 |
+
|
| 136 |
+
### Full Model Architecture
|
| 137 |
+
|
| 138 |
+
```
|
| 139 |
+
SentenceTransformer(
|
| 140 |
+
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: T5EncoderModel
|
| 141 |
+
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
|
| 142 |
+
)
|
| 143 |
+
```
|
| 144 |
+
|
| 145 |
+
## Usage
|
| 146 |
+
|
| 147 |
+
### Direct Usage (Sentence Transformers)
|
| 148 |
+
|
| 149 |
+
First install the Sentence Transformers library:
|
| 150 |
+
|
| 151 |
+
```bash
|
| 152 |
+
pip install -U sentence-transformers
|
| 153 |
+
```
|
| 154 |
+
|
| 155 |
+
Then you can load this model and run inference.
|
| 156 |
+
```python
|
| 157 |
+
from sentence_transformers import SentenceTransformer
|
| 158 |
+
|
| 159 |
+
# Download from the 🤗 Hub
|
| 160 |
+
model = SentenceTransformer("sentence_transformers_model_id")
|
| 161 |
+
# Run inference
|
| 162 |
+
sentences = [
|
| 163 |
+
'Look for funds that fit my stock holdings',
|
| 164 |
+
'Explore funds that match my stock portfolio',
|
| 165 |
+
'Can you tell me if my investments will grow well in the long run?',
|
| 166 |
+
]
|
| 167 |
+
embeddings = model.encode(sentences)
|
| 168 |
+
print(embeddings.shape)
|
| 169 |
+
# [3, 768]
|
| 170 |
+
|
| 171 |
+
# Get the similarity scores for the embeddings
|
| 172 |
+
similarities = model.similarity(embeddings, embeddings)
|
| 173 |
+
print(similarities.shape)
|
| 174 |
+
# [3, 3]
|
| 175 |
+
```
|
| 176 |
+
|
| 177 |
+
<!--
|
| 178 |
+
### Direct Usage (Transformers)
|
| 179 |
+
|
| 180 |
+
<details><summary>Click to see the direct usage in Transformers</summary>
|
| 181 |
+
|
| 182 |
+
</details>
|
| 183 |
+
-->
|
| 184 |
+
|
| 185 |
+
<!--
|
| 186 |
+
### Downstream Usage (Sentence Transformers)
|
| 187 |
+
|
| 188 |
+
You can finetune this model on your own dataset.
|
| 189 |
+
|
| 190 |
+
<details><summary>Click to expand</summary>
|
| 191 |
+
|
| 192 |
+
</details>
|
| 193 |
+
-->
|
| 194 |
+
|
| 195 |
+
<!--
|
| 196 |
+
### Out-of-Scope Use
|
| 197 |
+
|
| 198 |
+
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
|
| 199 |
+
-->
|
| 200 |
+
|
| 201 |
+
## Evaluation
|
| 202 |
+
|
| 203 |
+
### Metrics
|
| 204 |
+
|
| 205 |
+
#### Information Retrieval
|
| 206 |
+
|
| 207 |
+
* Dataset: `test-eval`
|
| 208 |
+
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
|
| 209 |
+
|
| 210 |
+
| Metric | Value |
|
| 211 |
+
|:--------------------|:-----------|
|
| 212 |
+
| cosine_accuracy@1 | 0.8659 |
|
| 213 |
+
| cosine_accuracy@3 | 0.9916 |
|
| 214 |
+
| cosine_accuracy@5 | 0.9972 |
|
| 215 |
+
| cosine_accuracy@10 | 1.0 |
|
| 216 |
+
| cosine_precision@1 | 0.8659 |
|
| 217 |
+
| cosine_precision@3 | 0.3305 |
|
| 218 |
+
| cosine_precision@5 | 0.1994 |
|
| 219 |
+
| cosine_precision@10 | 0.1 |
|
| 220 |
+
| cosine_recall@1 | 0.8659 |
|
| 221 |
+
| cosine_recall@3 | 0.9916 |
|
| 222 |
+
| cosine_recall@5 | 0.9972 |
|
| 223 |
+
| cosine_recall@10 | 1.0 |
|
| 224 |
+
| **cosine_ndcg@10** | **0.9461** |
|
| 225 |
+
| cosine_mrr@10 | 0.9274 |
|
| 226 |
+
| cosine_map@100 | 0.9274 |
|
| 227 |
+
|
| 228 |
+
<!--
|
| 229 |
+
## Bias, Risks and Limitations
|
| 230 |
+
|
| 231 |
+
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
|
| 232 |
+
-->
|
| 233 |
+
|
| 234 |
+
<!--
|
| 235 |
+
### Recommendations
|
| 236 |
+
|
| 237 |
+
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
|
| 238 |
+
-->
|
| 239 |
+
|
| 240 |
+
## Training Details
|
| 241 |
+
|
| 242 |
+
### Training Datasets
|
| 243 |
+
|
| 244 |
+
#### Unnamed Dataset
|
| 245 |
+
|
| 246 |
+
* Size: 1,785 training samples
|
| 247 |
+
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
|
| 248 |
+
* Approximate statistics based on the first 1000 samples:
|
| 249 |
+
| | sentence_0 | sentence_1 | label |
|
| 250 |
+
|:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------|
|
| 251 |
+
| type | string | string | float |
|
| 252 |
+
| details | <ul><li>min: 4 tokens</li><li>mean: 11.4 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 10.11 tokens</li><li>max: 33 tokens</li></ul> | <ul><li>min: 1.0</li><li>mean: 1.0</li><li>max: 1.0</li></ul> |
|
| 253 |
+
* Samples:
|
| 254 |
+
| sentence_0 | sentence_1 | label |
|
| 255 |
+
|:-------------------------------------------------------------------|:---------------------------------------------------------------------------|:-----------------|
|
| 256 |
+
| <code>How can I lower the risk in my investments?</code> | <code>How to reduce my risk </code> | <code>1.0</code> |
|
| 257 |
+
| <code>How is my asset allocation divided?</code> | <code>What is my asset allocation breakdown?</code> | <code>1.0</code> |
|
| 258 |
+
| <code>Any specific swap recommendations for better returns?</code> | <code>What are the specific swap suggestions to improve my returns?</code> | <code>1.0</code> |
|
| 259 |
+
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
|
| 260 |
+
```json
|
| 261 |
+
{
|
| 262 |
+
"scale": 20.0,
|
| 263 |
+
"similarity_fct": "cos_sim"
|
| 264 |
+
}
|
| 265 |
+
```
|
| 266 |
+
|
| 267 |
+
#### Unnamed Dataset
|
| 268 |
+
|
| 269 |
+
* Size: 1,785 training samples
|
| 270 |
+
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
|
| 271 |
+
* Approximate statistics based on the first 1000 samples:
|
| 272 |
+
| | sentence_0 | sentence_1 | label |
|
| 273 |
+
|:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:--------------------------------------------------------------|
|
| 274 |
+
| type | string | string | float |
|
| 275 |
+
| details | <ul><li>min: 4 tokens</li><li>mean: 11.28 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.98 tokens</li><li>max: 33 tokens</li></ul> | <ul><li>min: 1.0</li><li>mean: 1.0</li><li>max: 1.0</li></ul> |
|
| 276 |
+
* Samples:
|
| 277 |
+
| sentence_0 | sentence_1 | label |
|
| 278 |
+
|:----------------------------------------------------------------|:------------------------------------------------------|:-----------------|
|
| 279 |
+
| <code>What should I do to improve my investment returns?</code> | <code>How can I improve my returns?</code> | <code>1.0</code> |
|
| 280 |
+
| <code>Can you give me an overview of my portfolio?</code> | <code>Do you have any insights on my portfolio</code> | <code>1.0</code> |
|
| 281 |
+
| <code>Reveal my stock assets</code> | <code>Show my stocks</code> | <code>1.0</code> |
|
| 282 |
+
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
|
| 283 |
+
```json
|
| 284 |
+
{
|
| 285 |
+
"loss_fct": "torch.nn.modules.loss.MSELoss"
|
| 286 |
+
}
|
| 287 |
+
```
|
| 288 |
+
|
| 289 |
+
### Training Hyperparameters
|
| 290 |
+
#### Non-Default Hyperparameters
|
| 291 |
+
|
| 292 |
+
- `eval_strategy`: steps
|
| 293 |
+
- `per_device_train_batch_size`: 32
|
| 294 |
+
- `per_device_eval_batch_size`: 32
|
| 295 |
+
- `num_train_epochs`: 20
|
| 296 |
+
- `multi_dataset_batch_sampler`: round_robin
|
| 297 |
+
|
| 298 |
+
#### All Hyperparameters
|
| 299 |
+
<details><summary>Click to expand</summary>
|
| 300 |
+
|
| 301 |
+
- `overwrite_output_dir`: False
|
| 302 |
+
- `do_predict`: False
|
| 303 |
+
- `eval_strategy`: steps
|
| 304 |
+
- `prediction_loss_only`: True
|
| 305 |
+
- `per_device_train_batch_size`: 32
|
| 306 |
+
- `per_device_eval_batch_size`: 32
|
| 307 |
+
- `per_gpu_train_batch_size`: None
|
| 308 |
+
- `per_gpu_eval_batch_size`: None
|
| 309 |
+
- `gradient_accumulation_steps`: 1
|
| 310 |
+
- `eval_accumulation_steps`: None
|
| 311 |
+
- `torch_empty_cache_steps`: None
|
| 312 |
+
- `learning_rate`: 5e-05
|
| 313 |
+
- `weight_decay`: 0.0
|
| 314 |
+
- `adam_beta1`: 0.9
|
| 315 |
+
- `adam_beta2`: 0.999
|
| 316 |
+
- `adam_epsilon`: 1e-08
|
| 317 |
+
- `max_grad_norm`: 1
|
| 318 |
+
- `num_train_epochs`: 20
|
| 319 |
+
- `max_steps`: -1
|
| 320 |
+
- `lr_scheduler_type`: linear
|
| 321 |
+
- `lr_scheduler_kwargs`: {}
|
| 322 |
+
- `warmup_ratio`: 0.0
|
| 323 |
+
- `warmup_steps`: 0
|
| 324 |
+
- `log_level`: passive
|
| 325 |
+
- `log_level_replica`: warning
|
| 326 |
+
- `log_on_each_node`: True
|
| 327 |
+
- `logging_nan_inf_filter`: True
|
| 328 |
+
- `save_safetensors`: True
|
| 329 |
+
- `save_on_each_node`: False
|
| 330 |
+
- `save_only_model`: False
|
| 331 |
+
- `restore_callback_states_from_checkpoint`: False
|
| 332 |
+
- `no_cuda`: False
|
| 333 |
+
- `use_cpu`: False
|
| 334 |
+
- `use_mps_device`: False
|
| 335 |
+
- `seed`: 42
|
| 336 |
+
- `data_seed`: None
|
| 337 |
+
- `jit_mode_eval`: False
|
| 338 |
+
- `use_ipex`: False
|
| 339 |
+
- `bf16`: False
|
| 340 |
+
- `fp16`: False
|
| 341 |
+
- `fp16_opt_level`: O1
|
| 342 |
+
- `half_precision_backend`: auto
|
| 343 |
+
- `bf16_full_eval`: False
|
| 344 |
+
- `fp16_full_eval`: False
|
| 345 |
+
- `tf32`: None
|
| 346 |
+
- `local_rank`: 0
|
| 347 |
+
- `ddp_backend`: None
|
| 348 |
+
- `tpu_num_cores`: None
|
| 349 |
+
- `tpu_metrics_debug`: False
|
| 350 |
+
- `debug`: []
|
| 351 |
+
- `dataloader_drop_last`: False
|
| 352 |
+
- `dataloader_num_workers`: 0
|
| 353 |
+
- `dataloader_prefetch_factor`: None
|
| 354 |
+
- `past_index`: -1
|
| 355 |
+
- `disable_tqdm`: False
|
| 356 |
+
- `remove_unused_columns`: True
|
| 357 |
+
- `label_names`: None
|
| 358 |
+
- `load_best_model_at_end`: False
|
| 359 |
+
- `ignore_data_skip`: False
|
| 360 |
+
- `fsdp`: []
|
| 361 |
+
- `fsdp_min_num_params`: 0
|
| 362 |
+
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
|
| 363 |
+
- `tp_size`: 0
|
| 364 |
+
- `fsdp_transformer_layer_cls_to_wrap`: None
|
| 365 |
+
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
|
| 366 |
+
- `deepspeed`: None
|
| 367 |
+
- `label_smoothing_factor`: 0.0
|
| 368 |
+
- `optim`: adamw_torch
|
| 369 |
+
- `optim_args`: None
|
| 370 |
+
- `adafactor`: False
|
| 371 |
+
- `group_by_length`: False
|
| 372 |
+
- `length_column_name`: length
|
| 373 |
+
- `ddp_find_unused_parameters`: None
|
| 374 |
+
- `ddp_bucket_cap_mb`: None
|
| 375 |
+
- `ddp_broadcast_buffers`: False
|
| 376 |
+
- `dataloader_pin_memory`: True
|
| 377 |
+
- `dataloader_persistent_workers`: False
|
| 378 |
+
- `skip_memory_metrics`: True
|
| 379 |
+
- `use_legacy_prediction_loop`: False
|
| 380 |
+
- `push_to_hub`: False
|
| 381 |
+
- `resume_from_checkpoint`: None
|
| 382 |
+
- `hub_model_id`: None
|
| 383 |
+
- `hub_strategy`: every_save
|
| 384 |
+
- `hub_private_repo`: None
|
| 385 |
+
- `hub_always_push`: False
|
| 386 |
+
- `gradient_checkpointing`: False
|
| 387 |
+
- `gradient_checkpointing_kwargs`: None
|
| 388 |
+
- `include_inputs_for_metrics`: False
|
| 389 |
+
- `include_for_metrics`: []
|
| 390 |
+
- `eval_do_concat_batches`: True
|
| 391 |
+
- `fp16_backend`: auto
|
| 392 |
+
- `push_to_hub_model_id`: None
|
| 393 |
+
- `push_to_hub_organization`: None
|
| 394 |
+
- `mp_parameters`:
|
| 395 |
+
- `auto_find_batch_size`: False
|
| 396 |
+
- `full_determinism`: False
|
| 397 |
+
- `torchdynamo`: None
|
| 398 |
+
- `ray_scope`: last
|
| 399 |
+
- `ddp_timeout`: 1800
|
| 400 |
+
- `torch_compile`: False
|
| 401 |
+
- `torch_compile_backend`: None
|
| 402 |
+
- `torch_compile_mode`: None
|
| 403 |
+
- `include_tokens_per_second`: False
|
| 404 |
+
- `include_num_input_tokens_seen`: False
|
| 405 |
+
- `neftune_noise_alpha`: None
|
| 406 |
+
- `optim_target_modules`: None
|
| 407 |
+
- `batch_eval_metrics`: False
|
| 408 |
+
- `eval_on_start`: False
|
| 409 |
+
- `use_liger_kernel`: False
|
| 410 |
+
- `eval_use_gather_object`: False
|
| 411 |
+
- `average_tokens_across_devices`: False
|
| 412 |
+
- `prompts`: None
|
| 413 |
+
- `batch_sampler`: batch_sampler
|
| 414 |
+
- `multi_dataset_batch_sampler`: round_robin
|
| 415 |
+
|
| 416 |
+
</details>
|
| 417 |
+
|
| 418 |
+
### Training Logs
|
| 419 |
+
| Epoch | Step | Training Loss | test-eval_cosine_ndcg@10 |
|
| 420 |
+
|:-------:|:----:|:-------------:|:------------------------:|
|
| 421 |
+
| 1.0 | 112 | - | 0.9013 |
|
| 422 |
+
| 2.0 | 224 | - | 0.9112 |
|
| 423 |
+
| 3.0 | 336 | - | 0.9250 |
|
| 424 |
+
| 4.0 | 448 | - | 0.9307 |
|
| 425 |
+
| 4.4643 | 500 | 0.1949 | 0.9337 |
|
| 426 |
+
| 5.0 | 560 | - | 0.9342 |
|
| 427 |
+
| 6.0 | 672 | - | 0.9381 |
|
| 428 |
+
| 7.0 | 784 | - | 0.9423 |
|
| 429 |
+
| 8.0 | 896 | - | 0.9426 |
|
| 430 |
+
| 8.9286 | 1000 | 0.1347 | 0.9452 |
|
| 431 |
+
| 9.0 | 1008 | - | 0.9442 |
|
| 432 |
+
| 10.0 | 1120 | - | 0.9461 |
|
| 433 |
+
| 11.0 | 1232 | - | 0.9461 |
|
| 434 |
+
| 12.0 | 1344 | - | 0.9461 |
|
| 435 |
+
| 13.0 | 1456 | - | 0.9461 |
|
| 436 |
+
| 13.3929 | 1500 | 0.1193 | 0.9461 |
|
| 437 |
+
| 14.0 | 1568 | - | 0.9461 |
|
| 438 |
+
| 15.0 | 1680 | - | 0.9461 |
|
| 439 |
+
| 16.0 | 1792 | - | 0.9461 |
|
| 440 |
+
| 17.0 | 1904 | - | 0.9461 |
|
| 441 |
+
| 17.8571 | 2000 | 0.117 | 0.9461 |
|
| 442 |
+
| 18.0 | 2016 | - | 0.9461 |
|
| 443 |
+
| 19.0 | 2128 | - | 0.9461 |
|
| 444 |
+
|
| 445 |
+
|
| 446 |
+
### Framework Versions
|
| 447 |
+
- Python: 3.10.16
|
| 448 |
+
- Sentence Transformers: 4.1.0
|
| 449 |
+
- Transformers: 4.51.3
|
| 450 |
+
- PyTorch: 2.7.0
|
| 451 |
+
- Accelerate: 1.6.0
|
| 452 |
+
- Datasets: 3.5.0
|
| 453 |
+
- Tokenizers: 0.21.1
|
| 454 |
+
|
| 455 |
+
## Citation
|
| 456 |
+
|
| 457 |
+
### BibTeX
|
| 458 |
+
|
| 459 |
+
#### Sentence Transformers
|
| 460 |
+
```bibtex
|
| 461 |
+
@inproceedings{reimers-2019-sentence-bert,
|
| 462 |
+
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
|
| 463 |
+
author = "Reimers, Nils and Gurevych, Iryna",
|
| 464 |
+
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
|
| 465 |
+
month = "11",
|
| 466 |
+
year = "2019",
|
| 467 |
+
publisher = "Association for Computational Linguistics",
|
| 468 |
+
url = "https://arxiv.org/abs/1908.10084",
|
| 469 |
+
}
|
| 470 |
+
```
|
| 471 |
+
|
| 472 |
+
#### MultipleNegativesRankingLoss
|
| 473 |
+
```bibtex
|
| 474 |
+
@misc{henderson2017efficient,
|
| 475 |
+
title={Efficient Natural Language Response Suggestion for Smart Reply},
|
| 476 |
+
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
|
| 477 |
+
year={2017},
|
| 478 |
+
eprint={1705.00652},
|
| 479 |
+
archivePrefix={arXiv},
|
| 480 |
+
primaryClass={cs.CL}
|
| 481 |
+
}
|
| 482 |
+
```
|
| 483 |
+
|
| 484 |
+
<!--
|
| 485 |
+
## Glossary
|
| 486 |
+
|
| 487 |
+
*Clearly define terms in order to be accessible across audiences.*
|
| 488 |
+
-->
|
| 489 |
+
|
| 490 |
+
<!--
|
| 491 |
+
## Model Card Authors
|
| 492 |
+
|
| 493 |
+
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
|
| 494 |
+
-->
|
| 495 |
+
|
| 496 |
+
<!--
|
| 497 |
+
## Model Card Contact
|
| 498 |
+
|
| 499 |
+
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
|
| 500 |
+
-->
|
checkpoint-2240/config.json
ADDED
|
@@ -0,0 +1,63 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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| 1 |
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{
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|
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|
| 4 |
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|
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|
| 6 |
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|
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|
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|
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|
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|
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|
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|
| 16 |
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|
| 17 |
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|
| 18 |
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|
| 19 |
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|
| 20 |
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|
| 21 |
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|
| 22 |
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|
| 23 |
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|
| 24 |
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|
| 25 |
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|
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|
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|
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|
| 30 |
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|
| 31 |
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|
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|
| 33 |
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|
| 34 |
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|
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|
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|
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|
| 38 |
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|
| 39 |
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|
| 41 |
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|
| 42 |
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|
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| 44 |
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|
| 45 |
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|
| 46 |
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|
| 47 |
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|
| 48 |
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|
| 49 |
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|
| 50 |
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"prefix": "translate English to French: "
|
| 51 |
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|
| 52 |
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| 53 |
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|
| 54 |
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|
| 55 |
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|
| 56 |
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"prefix": "translate English to Romanian: "
|
| 57 |
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}
|
| 58 |
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|
| 59 |
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|
| 60 |
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|
| 61 |
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|
| 62 |
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"vocab_size": 32128
|
| 63 |
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}
|
checkpoint-2240/config_sentence_transformers.json
ADDED
|
@@ -0,0 +1,10 @@
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|
| 1 |
+
{
|
| 2 |
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"__version__": {
|
| 3 |
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"sentence_transformers": "4.1.0",
|
| 4 |
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"transformers": "4.51.3",
|
| 5 |
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|
| 6 |
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|
| 7 |
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|
| 8 |
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"default_prompt_name": null,
|
| 9 |
+
"similarity_fn_name": "cosine"
|
| 10 |
+
}
|
checkpoint-2240/model.safetensors
ADDED
|
@@ -0,0 +1,3 @@
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|
|
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| 1 |
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version https://git-lfs.github.com/spec/v1
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| 3 |
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size 438525864
|
checkpoint-2240/modules.json
ADDED
|
@@ -0,0 +1,14 @@
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|
| 1 |
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[
|
| 2 |
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{
|
| 3 |
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|
| 4 |
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"name": "0",
|
| 5 |
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|
| 6 |
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|
| 7 |
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|
| 8 |
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|
| 9 |
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|
| 10 |
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"name": "1",
|
| 11 |
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|
| 12 |
+
"type": "sentence_transformers.models.Pooling"
|
| 13 |
+
}
|
| 14 |
+
]
|
checkpoint-2240/optimizer.pt
ADDED
|
@@ -0,0 +1,3 @@
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| 1 |
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version https://git-lfs.github.com/spec/v1
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size 877109707
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checkpoint-2240/rng_state.pth
ADDED
|
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| 3 |
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size 14391
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checkpoint-2240/scheduler.pt
ADDED
|
@@ -0,0 +1,3 @@
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| 3 |
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size 1465
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checkpoint-2240/sentence_bert_config.json
ADDED
|
@@ -0,0 +1,4 @@
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|
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|
|
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|
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|
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|
| 1 |
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|
| 2 |
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|
| 3 |
+
"do_lower_case": false
|
| 4 |
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|
checkpoint-2240/special_tokens_map.json
ADDED
|
@@ -0,0 +1,125 @@
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|
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|
| 104 |
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|
| 105 |
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|
| 106 |
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|
| 107 |
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|
| 108 |
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|
| 109 |
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|
| 110 |
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|
| 111 |
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|
| 112 |
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|
| 113 |
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|
| 114 |
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|
| 115 |
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|
| 116 |
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|
| 117 |
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|
| 118 |
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"unk_token": {
|
| 119 |
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|
| 120 |
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|
| 121 |
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|
| 122 |
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|
| 123 |
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|
| 124 |
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|
| 125 |
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|
checkpoint-2240/tokenizer.json
ADDED
|
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|
|
checkpoint-2240/tokenizer_config.json
ADDED
|
@@ -0,0 +1,939 @@
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|
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|
| 1 |
+
{
|
| 2 |
+
"add_prefix_space": null,
|
| 3 |
+
"added_tokens_decoder": {
|
| 4 |
+
"0": {
|
| 5 |
+
"content": "<pad>",
|
| 6 |
+
"lstrip": false,
|
| 7 |
+
"normalized": false,
|
| 8 |
+
"rstrip": false,
|
| 9 |
+
"single_word": false,
|
| 10 |
+
"special": true
|
| 11 |
+
},
|
| 12 |
+
"1": {
|
| 13 |
+
"content": "</s>",
|
| 14 |
+
"lstrip": false,
|
| 15 |
+
"normalized": false,
|
| 16 |
+
"rstrip": false,
|
| 17 |
+
"single_word": false,
|
| 18 |
+
"special": true
|
| 19 |
+
},
|
| 20 |
+
"2": {
|
| 21 |
+
"content": "<unk>",
|
| 22 |
+
"lstrip": false,
|
| 23 |
+
"normalized": false,
|
| 24 |
+
"rstrip": false,
|
| 25 |
+
"single_word": false,
|
| 26 |
+
"special": true
|
| 27 |
+
},
|
| 28 |
+
"32000": {
|
| 29 |
+
"content": "<extra_id_99>",
|
| 30 |
+
"lstrip": false,
|
| 31 |
+
"normalized": false,
|
| 32 |
+
"rstrip": false,
|
| 33 |
+
"single_word": false,
|
| 34 |
+
"special": true
|
| 35 |
+
},
|
| 36 |
+
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|
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checkpoint-2240/trainer_state.json
ADDED
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@@ -0,0 +1,150 @@
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checkpoint-2240/training_args.bin
ADDED
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config.json
ADDED
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},
|
| 46 |
+
"translation_en_to_fr": {
|
| 47 |
+
"early_stopping": true,
|
| 48 |
+
"max_length": 300,
|
| 49 |
+
"num_beams": 4,
|
| 50 |
+
"prefix": "translate English to French: "
|
| 51 |
+
},
|
| 52 |
+
"translation_en_to_ro": {
|
| 53 |
+
"early_stopping": true,
|
| 54 |
+
"max_length": 300,
|
| 55 |
+
"num_beams": 4,
|
| 56 |
+
"prefix": "translate English to Romanian: "
|
| 57 |
+
}
|
| 58 |
+
},
|
| 59 |
+
"torch_dtype": "float32",
|
| 60 |
+
"transformers_version": "4.51.3",
|
| 61 |
+
"use_cache": true,
|
| 62 |
+
"vocab_size": 32128
|
| 63 |
+
}
|
config_sentence_transformers.json
ADDED
|
@@ -0,0 +1,10 @@
|
|
|
|
|
|
|
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|
| 1 |
+
{
|
| 2 |
+
"__version__": {
|
| 3 |
+
"sentence_transformers": "4.1.0",
|
| 4 |
+
"transformers": "4.51.3",
|
| 5 |
+
"pytorch": "2.7.0"
|
| 6 |
+
},
|
| 7 |
+
"prompts": {},
|
| 8 |
+
"default_prompt_name": null,
|
| 9 |
+
"similarity_fn_name": "cosine"
|
| 10 |
+
}
|
eval/Information-Retrieval_evaluation_test-eval_results.csv
ADDED
|
@@ -0,0 +1,284 @@
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accuracy@10,cosine-Precision@1,cosine-Recall@1,cosine-Precision@3,cosine-Recall@3,cosine-Precision@5,cosine-Recall@5,cosine-Precision@10,cosine-Recall@10,cosine-MRR@10,cosine-NDCG@10,cosine-MAP@100
|
| 2 |
+
1.0,112,0.7988826815642458,0.9385474860335196,0.9636871508379888,0.9888268156424581,0.7988826815642458,0.7988826815642458,0.3128491620111732,0.9385474860335196,0.19273743016759773,0.9636871508379888,0.09888268156424579,0.9888268156424581,0.8724971180278439,0.9013290962380638,0.8729215200848387
|
| 3 |
+
2.0,224,0.8100558659217877,0.952513966480447,0.9748603351955307,0.994413407821229,0.8100558659217877,0.8100558659217877,0.3175046554934823,0.952513966480447,0.1949720670391061,0.9748603351955307,0.09944134078212288,0.994413407821229,0.8835882770240309,0.9112287400862301,0.8838180481539645
|
| 4 |
+
3.0,336,0.8324022346368715,0.9664804469273743,0.9804469273743017,0.9972067039106145,0.8324022346368715,0.8324022346368715,0.3221601489757915,0.9664804469273743,0.1960893854748603,0.9804469273743017,0.09972067039106144,0.9972067039106145,0.9007637226212644,0.9249588498705006,0.9009499423605568
|
| 5 |
+
4.0,448,0.8435754189944135,0.9720670391061452,0.9888268156424581,1.0,0.8435754189944135,0.8435754189944135,0.3240223463687151,0.9720670391061452,0.19776536312849158,0.9888268156424581,0.09999999999999999,1.0,0.9074598740799856,0.930681103518929,0.9074598740799856
|
| 6 |
+
5.0,560,0.8519553072625698,0.9776536312849162,0.9888268156424581,0.9972067039106145,0.8519553072625698,0.8519553072625698,0.3258845437616387,0.9776536312849162,0.19776536312849158,0.9888268156424581,0.09972067039106144,0.9972067039106145,0.9128569211669768,0.9341576610559545,0.913110857175103
|
| 7 |
+
6.0,672,0.8547486033519553,0.9804469273743017,0.994413407821229,1.0,0.8547486033519553,0.8547486033519553,0.3268156424581006,0.9804469273743017,0.19888268156424577,0.994413407821229,0.09999999999999999,1.0,0.9171399751707012,0.9381337361421023,0.9171399751707014
|
| 8 |
+
7.0,784,0.8631284916201117,0.9832402234636871,0.994413407821229,1.0,0.8631284916201117,0.8631284916201117,0.32774674115456237,0.9832402234636871,0.19888268156424577,0.994413407821229,0.09999999999999999,1.0,0.922788640595903,0.9424281345517056,0.9227886405959032
|
| 9 |
+
8.0,896,0.8603351955307262,0.9916201117318436,0.9972067039106145,1.0,0.8603351955307262,0.8603351955307262,0.33054003724394787,0.9916201117318436,0.1994413407821229,0.9972067039106145,0.09999999999999999,1.0,0.9228385208832136,0.9426017872344871,0.9228385208832136
|
| 10 |
+
9.0,1008,0.8687150837988827,0.9916201117318436,1.0,1.0,0.8687150837988827,0.8687150837988827,0.33054003724394787,0.9916201117318436,0.19999999999999998,1.0,0.09999999999999999,1.0,0.9263966480446925,0.9452350119743832,0.9263966480446927
|
| 11 |
+
10.0,1120,0.8687150837988827,0.9916201117318436,1.0,1.0,0.8687150837988827,0.8687150837988827,0.33054003724394787,0.9916201117318436,0.19999999999999998,1.0,0.09999999999999999,1.0,0.9263966480446925,0.9452350119743832,0.9263966480446927
|
| 12 |
+
1.0,112,0.7988826815642458,0.9385474860335196,0.9636871508379888,0.9888268156424581,0.7988826815642458,0.7988826815642458,0.3128491620111732,0.9385474860335196,0.19273743016759773,0.9636871508379888,0.09888268156424579,0.9888268156424581,0.8724971180278439,0.9013290962380638,0.8729215200848387
|
| 13 |
+
2.0,224,0.8100558659217877,0.952513966480447,0.9748603351955307,0.994413407821229,0.8100558659217877,0.8100558659217877,0.3175046554934823,0.952513966480447,0.1949720670391061,0.9748603351955307,0.09944134078212288,0.994413407821229,0.8835882770240309,0.9112287400862301,0.8838180481539645
|
| 14 |
+
3.0,336,0.8324022346368715,0.9664804469273743,0.9804469273743017,0.9972067039106145,0.8324022346368715,0.8324022346368715,0.3221601489757915,0.9664804469273743,0.1960893854748603,0.9804469273743017,0.09972067039106144,0.9972067039106145,0.9007637226212644,0.9249588498705006,0.9009499423605568
|
| 15 |
+
4.0,448,0.8435754189944135,0.9720670391061452,0.9888268156424581,1.0,0.8435754189944135,0.8435754189944135,0.3240223463687151,0.9720670391061452,0.19776536312849158,0.9888268156424581,0.09999999999999999,1.0,0.9074598740799856,0.930681103518929,0.9074598740799856
|
| 16 |
+
4.464285714285714,500,0.8519553072625698,0.9748603351955307,0.9888268156424581,1.0,0.8519553072625698,0.8519553072625698,0.3249534450651769,0.9748603351955307,0.19776536312849158,0.9888268156424581,0.09999999999999999,1.0,0.9115068280571073,0.9336742870997445,0.9115068280571075
|
| 17 |
+
5.0,560,0.8519553072625698,0.9776536312849162,0.9888268156424581,0.9972067039106145,0.8519553072625698,0.8519553072625698,0.3258845437616387,0.9776536312849162,0.19776536312849158,0.9888268156424581,0.09972067039106144,0.9972067039106145,0.9128569211669768,0.9341576610559545,0.913110857175103
|
| 18 |
+
6.0,672,0.8547486033519553,0.9804469273743017,0.994413407821229,1.0,0.8547486033519553,0.8547486033519553,0.3268156424581006,0.9804469273743017,0.19888268156424577,0.994413407821229,0.09999999999999999,1.0,0.9171399751707012,0.9381337361421023,0.9171399751707014
|
| 19 |
+
7.0,784,0.8631284916201117,0.9832402234636871,0.9916201117318436,1.0,0.8631284916201117,0.8631284916201117,0.32774674115456237,0.9832402234636871,0.19832402234636867,0.9916201117318436,0.09999999999999999,1.0,0.922695530726257,0.9423425322608494,0.922695530726257
|
| 20 |
+
8.0,896,0.8603351955307262,0.9916201117318436,0.9972067039106145,1.0,0.8603351955307262,0.8603351955307262,0.33054003724394787,0.9916201117318436,0.1994413407821229,0.9972067039106145,0.09999999999999999,1.0,0.9228385208832136,0.9426017872344871,0.9228385208832136
|
| 21 |
+
8.928571428571429,1000,0.8687150837988827,0.9916201117318436,1.0,1.0,0.8687150837988827,0.8687150837988827,0.33054003724394787,0.9916201117318436,0.19999999999999998,1.0,0.09999999999999999,1.0,0.9263966480446925,0.9452350119743832,0.9263966480446927
|
| 22 |
+
9.0,1008,0.8687150837988827,0.9916201117318436,1.0,1.0,0.8687150837988827,0.8687150837988827,0.33054003724394787,0.9916201117318436,0.19999999999999998,1.0,0.09999999999999999,1.0,0.9263966480446925,0.9452350119743832,0.9263966480446927
|
| 23 |
+
10.0,1120,0.8687150837988827,0.9916201117318436,1.0,1.0,0.8687150837988827,0.8687150837988827,0.33054003724394787,0.9916201117318436,0.19999999999999998,1.0,0.09999999999999999,1.0,0.9263966480446925,0.9452350119743832,0.9263966480446927
|
| 24 |
+
1.0,112,0.7988826815642458,0.9385474860335196,0.9636871508379888,0.9888268156424581,0.7988826815642458,0.7988826815642458,0.3128491620111732,0.9385474860335196,0.19273743016759773,0.9636871508379888,0.09888268156424579,0.9888268156424581,0.8724971180278439,0.9013290962380638,0.8729215200848387
|
| 25 |
+
2.0,224,0.8100558659217877,0.952513966480447,0.9748603351955307,0.994413407821229,0.8100558659217877,0.8100558659217877,0.3175046554934823,0.952513966480447,0.1949720670391061,0.9748603351955307,0.09944134078212288,0.994413407821229,0.8835882770240309,0.9112287400862301,0.8838180481539645
|
| 26 |
+
3.0,336,0.8324022346368715,0.9664804469273743,0.9804469273743017,0.9972067039106145,0.8324022346368715,0.8324022346368715,0.3221601489757915,0.9664804469273743,0.1960893854748603,0.9804469273743017,0.09972067039106144,0.9972067039106145,0.9007637226212644,0.9249588498705006,0.9009499423605568
|
| 27 |
+
4.0,448,0.8435754189944135,0.9720670391061452,0.9888268156424581,1.0,0.8435754189944135,0.8435754189944135,0.3240223463687151,0.9720670391061452,0.19776536312849158,0.9888268156424581,0.09999999999999999,1.0,0.9074598740799856,0.930681103518929,0.9074598740799856
|
| 28 |
+
4.464285714285714,500,0.8519553072625698,0.9748603351955307,0.9888268156424581,1.0,0.8519553072625698,0.8519553072625698,0.3249534450651769,0.9748603351955307,0.19776536312849158,0.9888268156424581,0.09999999999999999,1.0,0.9115068280571073,0.9336742870997445,0.9115068280571075
|
| 29 |
+
5.0,560,0.8519553072625698,0.9776536312849162,0.9888268156424581,0.9972067039106145,0.8519553072625698,0.8519553072625698,0.3258845437616387,0.9776536312849162,0.19776536312849158,0.9888268156424581,0.09972067039106144,0.9972067039106145,0.9128569211669768,0.9341576610559545,0.913110857175103
|
| 30 |
+
6.0,672,0.8547486033519553,0.9804469273743017,0.994413407821229,1.0,0.8547486033519553,0.8547486033519553,0.3268156424581006,0.9804469273743017,0.19888268156424577,0.994413407821229,0.09999999999999999,1.0,0.9171399751707012,0.9381337361421023,0.9171399751707014
|
| 31 |
+
7.0,784,0.8631284916201117,0.9832402234636871,0.9916201117318436,1.0,0.8631284916201117,0.8631284916201117,0.32774674115456237,0.9832402234636871,0.19832402234636867,0.9916201117318436,0.09999999999999999,1.0,0.922695530726257,0.9423425322608494,0.922695530726257
|
| 32 |
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14.0,1568,0.8659217877094972,0.9916201117318436,0.9972067039106145,1.0,0.8659217877094972,0.8659217877094972,0.33054003724394787,0.9916201117318436,0.1994413407821229,0.9972067039106145,0.09999999999999999,1.0,0.9273743016759775,0.9460695277624867,0.9273743016759777
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| 81 |
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| 82 |
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| 83 |
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| 84 |
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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size 438525864
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modules.json
ADDED
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@@ -0,0 +1,14 @@
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[
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{
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"name": "1",
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"type": "sentence_transformers.models.Pooling"
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sentence_bert_config.json
ADDED
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{
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special_tokens_map.json
ADDED
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{
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| 16 |
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"<extra_id_13>",
|
| 17 |
+
"<extra_id_14>",
|
| 18 |
+
"<extra_id_15>",
|
| 19 |
+
"<extra_id_16>",
|
| 20 |
+
"<extra_id_17>",
|
| 21 |
+
"<extra_id_18>",
|
| 22 |
+
"<extra_id_19>",
|
| 23 |
+
"<extra_id_20>",
|
| 24 |
+
"<extra_id_21>",
|
| 25 |
+
"<extra_id_22>",
|
| 26 |
+
"<extra_id_23>",
|
| 27 |
+
"<extra_id_24>",
|
| 28 |
+
"<extra_id_25>",
|
| 29 |
+
"<extra_id_26>",
|
| 30 |
+
"<extra_id_27>",
|
| 31 |
+
"<extra_id_28>",
|
| 32 |
+
"<extra_id_29>",
|
| 33 |
+
"<extra_id_30>",
|
| 34 |
+
"<extra_id_31>",
|
| 35 |
+
"<extra_id_32>",
|
| 36 |
+
"<extra_id_33>",
|
| 37 |
+
"<extra_id_34>",
|
| 38 |
+
"<extra_id_35>",
|
| 39 |
+
"<extra_id_36>",
|
| 40 |
+
"<extra_id_37>",
|
| 41 |
+
"<extra_id_38>",
|
| 42 |
+
"<extra_id_39>",
|
| 43 |
+
"<extra_id_40>",
|
| 44 |
+
"<extra_id_41>",
|
| 45 |
+
"<extra_id_42>",
|
| 46 |
+
"<extra_id_43>",
|
| 47 |
+
"<extra_id_44>",
|
| 48 |
+
"<extra_id_45>",
|
| 49 |
+
"<extra_id_46>",
|
| 50 |
+
"<extra_id_47>",
|
| 51 |
+
"<extra_id_48>",
|
| 52 |
+
"<extra_id_49>",
|
| 53 |
+
"<extra_id_50>",
|
| 54 |
+
"<extra_id_51>",
|
| 55 |
+
"<extra_id_52>",
|
| 56 |
+
"<extra_id_53>",
|
| 57 |
+
"<extra_id_54>",
|
| 58 |
+
"<extra_id_55>",
|
| 59 |
+
"<extra_id_56>",
|
| 60 |
+
"<extra_id_57>",
|
| 61 |
+
"<extra_id_58>",
|
| 62 |
+
"<extra_id_59>",
|
| 63 |
+
"<extra_id_60>",
|
| 64 |
+
"<extra_id_61>",
|
| 65 |
+
"<extra_id_62>",
|
| 66 |
+
"<extra_id_63>",
|
| 67 |
+
"<extra_id_64>",
|
| 68 |
+
"<extra_id_65>",
|
| 69 |
+
"<extra_id_66>",
|
| 70 |
+
"<extra_id_67>",
|
| 71 |
+
"<extra_id_68>",
|
| 72 |
+
"<extra_id_69>",
|
| 73 |
+
"<extra_id_70>",
|
| 74 |
+
"<extra_id_71>",
|
| 75 |
+
"<extra_id_72>",
|
| 76 |
+
"<extra_id_73>",
|
| 77 |
+
"<extra_id_74>",
|
| 78 |
+
"<extra_id_75>",
|
| 79 |
+
"<extra_id_76>",
|
| 80 |
+
"<extra_id_77>",
|
| 81 |
+
"<extra_id_78>",
|
| 82 |
+
"<extra_id_79>",
|
| 83 |
+
"<extra_id_80>",
|
| 84 |
+
"<extra_id_81>",
|
| 85 |
+
"<extra_id_82>",
|
| 86 |
+
"<extra_id_83>",
|
| 87 |
+
"<extra_id_84>",
|
| 88 |
+
"<extra_id_85>",
|
| 89 |
+
"<extra_id_86>",
|
| 90 |
+
"<extra_id_87>",
|
| 91 |
+
"<extra_id_88>",
|
| 92 |
+
"<extra_id_89>",
|
| 93 |
+
"<extra_id_90>",
|
| 94 |
+
"<extra_id_91>",
|
| 95 |
+
"<extra_id_92>",
|
| 96 |
+
"<extra_id_93>",
|
| 97 |
+
"<extra_id_94>",
|
| 98 |
+
"<extra_id_95>",
|
| 99 |
+
"<extra_id_96>",
|
| 100 |
+
"<extra_id_97>",
|
| 101 |
+
"<extra_id_98>",
|
| 102 |
+
"<extra_id_99>"
|
| 103 |
+
],
|
| 104 |
+
"eos_token": {
|
| 105 |
+
"content": "</s>",
|
| 106 |
+
"lstrip": false,
|
| 107 |
+
"normalized": false,
|
| 108 |
+
"rstrip": false,
|
| 109 |
+
"single_word": false
|
| 110 |
+
},
|
| 111 |
+
"pad_token": {
|
| 112 |
+
"content": "<pad>",
|
| 113 |
+
"lstrip": false,
|
| 114 |
+
"normalized": false,
|
| 115 |
+
"rstrip": false,
|
| 116 |
+
"single_word": false
|
| 117 |
+
},
|
| 118 |
+
"unk_token": {
|
| 119 |
+
"content": "<unk>",
|
| 120 |
+
"lstrip": false,
|
| 121 |
+
"normalized": false,
|
| 122 |
+
"rstrip": false,
|
| 123 |
+
"single_word": false
|
| 124 |
+
}
|
| 125 |
+
}
|
tokenizer.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
tokenizer_config.json
ADDED
|
@@ -0,0 +1,939 @@
|
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| 1 |
+
{
|
| 2 |
+
"add_prefix_space": null,
|
| 3 |
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"added_tokens_decoder": {
|
| 4 |
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"0": {
|
| 5 |
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| 6 |
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| 7 |
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| 8 |
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|
| 9 |
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| 10 |
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|
| 11 |
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| 12 |
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|
| 13 |
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|
| 14 |
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| 15 |
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|
| 16 |
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| 17 |
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|
| 18 |
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"special": true
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| 19 |
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| 20 |
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"2": {
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| 21 |
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|
| 22 |
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| 23 |
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| 24 |
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| 25 |
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| 26 |
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|
| 27 |
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| 28 |
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| 29 |
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| 30 |
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| 31 |
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| 32 |
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| 33 |
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| 34 |
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|
| 35 |
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| 36 |
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|
| 37 |
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|
| 38 |
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| 39 |
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| 40 |
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| 41 |
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| 42 |
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| 43 |
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| 44 |
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| 45 |
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| 46 |
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| 47 |
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| 48 |
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| 49 |
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| 50 |
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| 51 |
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| 52 |
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| 53 |
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|
| 54 |
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| 55 |
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| 56 |
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| 57 |
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| 58 |
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| 59 |
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| 60 |
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| 61 |
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| 62 |
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| 63 |
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| 64 |
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| 65 |
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| 66 |
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| 67 |
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| 68 |
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| 69 |
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| 70 |
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| 71 |
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| 72 |
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| 73 |
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| 74 |
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| 75 |
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| 76 |
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|
| 77 |
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| 78 |
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| 79 |
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| 80 |
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| 81 |
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| 82 |
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| 83 |
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| 84 |
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| 85 |
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| 86 |
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| 87 |
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| 88 |
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| 89 |
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| 90 |
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| 91 |
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| 92 |
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|
| 93 |
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|
| 94 |
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| 95 |
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| 96 |
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| 97 |
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| 98 |
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|
| 99 |
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| 100 |
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|
| 101 |
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|
| 102 |
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| 103 |
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| 104 |
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| 105 |
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|
| 106 |
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|
| 107 |
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| 108 |
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|
| 109 |
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| 110 |
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| 111 |
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| 112 |
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| 113 |
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| 114 |
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| 115 |
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| 116 |
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| 117 |
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| 118 |
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| 119 |
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| 120 |
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| 121 |
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| 122 |
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|
| 123 |
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| 124 |
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| 125 |
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| 126 |
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| 127 |
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| 128 |
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| 129 |
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| 130 |
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| 131 |
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| 132 |
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| 133 |
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| 134 |
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| 135 |
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| 136 |
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| 137 |
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| 138 |
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| 139 |
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| 140 |
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| 141 |
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| 142 |
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| 143 |
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| 144 |
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| 145 |
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| 146 |
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|
| 147 |
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| 148 |
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|
| 149 |
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| 150 |
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| 151 |
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| 152 |
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| 153 |
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| 154 |
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|
| 155 |
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| 156 |
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|
| 157 |
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| 158 |
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| 159 |
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| 160 |
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| 161 |
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| 162 |
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| 163 |
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| 164 |
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| 165 |
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| 166 |
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| 167 |
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| 168 |
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| 169 |
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| 170 |
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| 171 |
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| 172 |
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| 173 |
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| 174 |
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| 175 |
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| 176 |
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| 177 |
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| 178 |
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| 179 |
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| 180 |
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| 181 |
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| 182 |
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| 183 |
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| 184 |
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| 185 |
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| 186 |
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| 187 |
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| 188 |
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| 189 |
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| 190 |
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| 191 |
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| 192 |
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| 193 |
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| 194 |
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| 195 |
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| 196 |
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|
| 197 |
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|
| 198 |
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| 199 |
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| 200 |
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| 201 |
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| 202 |
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|
| 203 |
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| 204 |
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|
| 205 |
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|
| 206 |
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| 207 |
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| 208 |
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| 210 |
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| 211 |
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| 212 |
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| 213 |
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| 214 |
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| 216 |
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| 218 |
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| 219 |
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| 220 |
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| 221 |
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| 222 |
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| 223 |
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| 225 |
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| 226 |
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| 227 |
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| 228 |
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| 229 |
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| 234 |
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| 235 |
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| 244 |
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| 283 |
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| 284 |
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|
| 736 |
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|
| 737 |
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|
| 738 |
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"special": true
|
| 739 |
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},
|
| 740 |
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"32089": {
|
| 741 |
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"content": "<extra_id_10>",
|
| 742 |
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|
| 743 |
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|
| 744 |
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|
| 745 |
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|
| 746 |
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|
| 747 |
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},
|
| 748 |
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"32090": {
|
| 749 |
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"content": "<extra_id_9>",
|
| 750 |
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|
| 751 |
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|
| 752 |
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|
| 753 |
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|
| 754 |
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|
| 755 |
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},
|
| 756 |
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|
| 757 |
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"content": "<extra_id_8>",
|
| 758 |
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|
| 759 |
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|
| 760 |
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|
| 761 |
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|
| 762 |
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"special": true
|
| 763 |
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},
|
| 764 |
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|
| 765 |
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"content": "<extra_id_7>",
|
| 766 |
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|
| 767 |
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|
| 768 |
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|
| 769 |
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|
| 770 |
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|
| 771 |
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},
|
| 772 |
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|
| 773 |
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"content": "<extra_id_6>",
|
| 774 |
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|
| 775 |
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|
| 776 |
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|
| 777 |
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|
| 778 |
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|
| 779 |
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},
|
| 780 |
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|
| 781 |
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"content": "<extra_id_5>",
|
| 782 |
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|
| 783 |
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|
| 784 |
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| 785 |
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|
| 786 |
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|
| 787 |
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},
|
| 788 |
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|
| 789 |
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|
| 790 |
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|
| 791 |
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|
| 792 |
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|
| 793 |
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|
| 794 |
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"special": true
|
| 795 |
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},
|
| 796 |
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"32096": {
|
| 797 |
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"content": "<extra_id_3>",
|
| 798 |
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|
| 799 |
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|
| 800 |
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|
| 801 |
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|
| 802 |
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"special": true
|
| 803 |
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},
|
| 804 |
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|
| 805 |
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"content": "<extra_id_2>",
|
| 806 |
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|
| 807 |
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|
| 808 |
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|
| 809 |
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|
| 810 |
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"special": true
|
| 811 |
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},
|
| 812 |
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"32098": {
|
| 813 |
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"content": "<extra_id_1>",
|
| 814 |
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| 815 |
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|
| 816 |
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|
| 817 |
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|
| 818 |
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"special": true
|
| 819 |
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},
|
| 820 |
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"32099": {
|
| 821 |
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"content": "<extra_id_0>",
|
| 822 |
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|
| 823 |
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|
| 824 |
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|
| 825 |
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|
| 826 |
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"special": true
|
| 827 |
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}
|
| 828 |
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},
|
| 829 |
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"additional_special_tokens": [
|
| 830 |
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"<extra_id_0>",
|
| 831 |
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"<extra_id_1>",
|
| 832 |
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"<extra_id_2>",
|
| 833 |
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"<extra_id_3>",
|
| 834 |
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"<extra_id_4>",
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| 835 |
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"<extra_id_5>",
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| 836 |
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"<extra_id_6>",
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| 837 |
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"<extra_id_7>",
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| 838 |
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"<extra_id_8>",
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| 839 |
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"<extra_id_9>",
|
| 840 |
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"<extra_id_10>",
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| 841 |
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"<extra_id_11>",
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| 842 |
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"<extra_id_12>",
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| 843 |
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"<extra_id_13>",
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| 844 |
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"<extra_id_14>",
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| 845 |
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| 846 |
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| 847 |
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| 848 |
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"<extra_id_18>",
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| 849 |
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"<extra_id_19>",
|
| 850 |
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"<extra_id_20>",
|
| 851 |
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"<extra_id_21>",
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| 852 |
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"<extra_id_22>",
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| 853 |
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"<extra_id_23>",
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| 854 |
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"<extra_id_24>",
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| 855 |
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| 856 |
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| 857 |
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"<extra_id_27>",
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| 858 |
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"<extra_id_28>",
|
| 859 |
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"<extra_id_29>",
|
| 860 |
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"<extra_id_30>",
|
| 861 |
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"<extra_id_31>",
|
| 862 |
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"<extra_id_32>",
|
| 863 |
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"<extra_id_33>",
|
| 864 |
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|
| 865 |
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|
| 866 |
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"<extra_id_36>",
|
| 867 |
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"<extra_id_37>",
|
| 868 |
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| 869 |
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|
| 870 |
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| 871 |
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|
| 872 |
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"<extra_id_42>",
|
| 873 |
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"<extra_id_43>",
|
| 874 |
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"<extra_id_44>",
|
| 875 |
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"<extra_id_45>",
|
| 876 |
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"<extra_id_46>",
|
| 877 |
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"<extra_id_47>",
|
| 878 |
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"<extra_id_48>",
|
| 879 |
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"<extra_id_49>",
|
| 880 |
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"<extra_id_50>",
|
| 881 |
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"<extra_id_51>",
|
| 882 |
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"<extra_id_52>",
|
| 883 |
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"<extra_id_53>",
|
| 884 |
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"<extra_id_54>",
|
| 885 |
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"<extra_id_55>",
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| 886 |
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"<extra_id_56>",
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| 887 |
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"<extra_id_57>",
|
| 888 |
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"<extra_id_58>",
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| 889 |
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"<extra_id_59>",
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| 890 |
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|
| 891 |
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| 892 |
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"<extra_id_62>",
|
| 893 |
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"<extra_id_63>",
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| 894 |
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"<extra_id_64>",
|
| 895 |
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"<extra_id_65>",
|
| 896 |
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"<extra_id_66>",
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| 897 |
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"<extra_id_67>",
|
| 898 |
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"<extra_id_68>",
|
| 899 |
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"<extra_id_69>",
|
| 900 |
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"<extra_id_70>",
|
| 901 |
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"<extra_id_71>",
|
| 902 |
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"<extra_id_72>",
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| 903 |
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"<extra_id_73>",
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| 904 |
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"<extra_id_74>",
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| 905 |
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"<extra_id_75>",
|
| 906 |
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"<extra_id_76>",
|
| 907 |
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"<extra_id_77>",
|
| 908 |
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"<extra_id_78>",
|
| 909 |
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"<extra_id_79>",
|
| 910 |
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"<extra_id_80>",
|
| 911 |
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"<extra_id_81>",
|
| 912 |
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"<extra_id_82>",
|
| 913 |
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"<extra_id_83>",
|
| 914 |
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"<extra_id_84>",
|
| 915 |
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"<extra_id_85>",
|
| 916 |
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"<extra_id_86>",
|
| 917 |
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"<extra_id_87>",
|
| 918 |
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"<extra_id_88>",
|
| 919 |
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"<extra_id_89>",
|
| 920 |
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"<extra_id_90>",
|
| 921 |
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"<extra_id_91>",
|
| 922 |
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"<extra_id_92>",
|
| 923 |
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"<extra_id_93>",
|
| 924 |
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"<extra_id_94>",
|
| 925 |
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"<extra_id_95>",
|
| 926 |
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"<extra_id_96>",
|
| 927 |
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"<extra_id_97>",
|
| 928 |
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"<extra_id_98>",
|
| 929 |
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"<extra_id_99>"
|
| 930 |
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],
|
| 931 |
+
"clean_up_tokenization_spaces": true,
|
| 932 |
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"eos_token": "</s>",
|
| 933 |
+
"extra_ids": 100,
|
| 934 |
+
"extra_special_tokens": {},
|
| 935 |
+
"model_max_length": 512,
|
| 936 |
+
"pad_token": "<pad>",
|
| 937 |
+
"tokenizer_class": "T5Tokenizer",
|
| 938 |
+
"unk_token": "<unk>"
|
| 939 |
+
}
|