Add new SentenceTransformer model

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": true,
+  "pooling_mode_mean_tokens": false,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

README.md

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+---
+language:
+- en
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:6448
+- loss:MultipleNegativesRankingLoss
+base_model: BAAI/bge-base-en-v1.5
+widget:
+- source_sentence: How are retail sales data integrated into trading models?
+  sentences:
+  - Lagged variables represent historical values of a time series variable and are
+    used in forecasting models to capture the impact of past observations on future
+    market trends, enhancing the accuracy of predictions by incorporating relevant
+    historical information.
+  - Retail sales data reflect consumer spending patterns and overall economic activity.
+    Traders analyze this indicator to gauge consumer confidence, sectoral performance,
+    and potential market trends related to retail-focused stocks.
+  - Regulatory approval for a new drug can have a positive impact on a pharmaceutical
+    company's stock price as it opens up new revenue streams and market opportunities.
+- source_sentence: What impact does algorithmic trading have on market liquidity?
+  sentences:
+  - Volume analysis in stock trading involves studying the number of shares or contracts
+    traded in a security or market over a specific period to gauge the strength or
+    weakness of a price move.
+  - Social media sentiment analysis can assist in detecting anomalies in stock prices
+    by capturing public sentiment and opinions on stocks, identifying trends or sudden
+    shifts in sentiment that may precede abnormal price movements.
+  - Algorithmic trading can impact market liquidity by increasing trading speed, efficiency,
+    and overall trading volume, leading to potential liquidity disruptions during
+    certain market conditions.
+- source_sentence: What considerations should traders take into account when selecting
+    an adaptive trading algorithm?
+  sentences:
+  - Historical price data helps analysts identify patterns and trends that can be
+    used to develop models for predicting future stock prices based on past performance.
+  - Traders should consider factors such as performance metrics, risk management capabilities,
+    adaptability to changing market conditions, data requirements, and the level of
+    transparency and control offered by the algorithm.
+  - A stock exchange is a centralized marketplace where securities like stocks, bonds,
+    and commodities are bought and sold by investors and traders.
+- source_sentence: How can currency exchange rates and forex markets be integrated
+    into trading models alongside macroeconomic indicators?
+  sentences:
+  - Moving averages smooth out price data over a specified period, making it easier
+    to identify trends and reversals in stock prices.
+  - Currency exchange rates and forex markets are integrated into trading models to
+    assess currency risk, international trade impact, and cross-border investment
+    opportunities influenced by macroeconomic indicators.
+  - Investors use quantitative momentum indicators to identify securities that are
+    gaining positive momentum and potentially generating profits by buying those assets
+    and selling underperforming assets.
+- source_sentence: What role does back-testing play in refining event-driven trading
+    strategies using historical data and real-time analysis?
+  sentences:
+  - Genetic algorithms are well-suited for solving multi-objective optimization problems,
+    nonlinear and non-convex optimization problems, problems with high-dimensional
+    search spaces, and problems where traditional methods may struggle to find optimal
+    solutions.
+  - Risk management techniques such as position sizing, portfolio diversification,
+    and stop-loss orders are often used in quantitative momentum strategies to manage
+    downside risk and protect against large losses.
+  - Back-testing allows traders to evaluate the performance of event-driven trading
+    strategies using historical data, identify patterns, optimize parameters, and
+    refine strategies for real-time implementation.
+datasets:
+- yymYYM/stock_trading_QA
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+metrics:
+- cosine_accuracy@3
+- cosine_precision@3
+- cosine_recall@3
+- cosine_ndcg@3
+- cosine_mrr@3
+- cosine_map@3
+model-index:
+- name: SentenceTransformer based on BAAI/bge-base-en-v1.5
+  results:
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: Unknown
+      type: unknown
+    metrics:
+    - type: cosine_accuracy@3
+      value: 0.6750348675034867
+      name: Cosine Accuracy@3
+    - type: cosine_precision@3
+      value: 0.22501162250116222
+      name: Cosine Precision@3
+    - type: cosine_recall@3
+      value: 0.6750348675034867
+      name: Cosine Recall@3
+    - type: cosine_ndcg@3
+      value: 0.5838116811117793
+      name: Cosine Ndcg@3
+    - type: cosine_mrr@3
+      value: 0.5523012552301251
+      name: Cosine Mrr@3
+    - type: cosine_map@3
+      value: 0.5523012552301255
+      name: Cosine Map@3
+---
+
+# SentenceTransformer based on BAAI/bge-base-en-v1.5
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) on the [stock_trading_qa](https://huggingface.co/datasets/yymYYM/stock_trading_QA) dataset. 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.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 768 dimensions
+- **Similarity Function:** Cosine Similarity
+- **Training Dataset:**
+    - [stock_trading_qa](https://huggingface.co/datasets/yymYYM/stock_trading_QA)
+- **Language:** en
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel 
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+  (2): Normalize()
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("iamleonie/leonies-test")
+# Run inference
+sentences = [
+    'What role does back-testing play in refining event-driven trading strategies using historical data and real-time analysis?',
+    'Back-testing allows traders to evaluate the performance of event-driven trading strategies using historical data, identify patterns, optimize parameters, and refine strategies for real-time implementation.',
+    'Risk management techniques such as position sizing, portfolio diversification, and stop-loss orders are often used in quantitative momentum strategies to manage downside risk and protect against large losses.',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 768]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+## Evaluation
+
+### Metrics
+
+#### Information Retrieval
+
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric             | Value      |
+|:-------------------|:-----------|
+| cosine_accuracy@3  | 0.675      |
+| cosine_precision@3 | 0.225      |
+| cosine_recall@3    | 0.675      |
+| **cosine_ndcg@3**  | **0.5838** |
+| cosine_mrr@3       | 0.5523     |
+| cosine_map@3       | 0.5523     |
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### stock_trading_qa
+
+* Dataset: [stock_trading_qa](https://huggingface.co/datasets/yymYYM/stock_trading_QA) at [35dab2e](https://huggingface.co/datasets/yymYYM/stock_trading_QA/tree/35dab2e25b6da10842cfb0f832b715cab3765727)
+* Size: 6,448 training samples
+* Columns: <code>anchor</code> and <code>context</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                            | context                                                                            |
+  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                             |
+  | details | <ul><li>min: 7 tokens</li><li>mean: 15.83 tokens</li><li>max: 39 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 34.67 tokens</li><li>max: 59 tokens</li></ul> |
+* Samples:
+  | anchor                                                                                                                                    | context                                                                                                                                                                                                                                               |
+  |:------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>How should I approach investing in a volatile stock market?</code>                                                                  | <code>Diversify your portfolio, invest in stable companies, consider dollar-cost averaging, and stay informed about market trends to make informed trading decisions.</code>                                                                          |
+  | <code>What is the role of cross-validation in assessing the performance of time series forecasting models for stock market trends?</code> | <code>Cross-validation helps evaluate the generalization ability of forecasting models by partitioning historical data into training and validation sets, ensuring that the model's performance is robust and reliable for future predictions.</code> |
+  | <code>What role does correlation play in statistical arbitrage and pair trading?</code>                                                   | <code>Correlation measures the relationship between asset prices and helps traders identify pairs that exhibit a stable price relationship suitable for pair trading.</code>                                                                          |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim"
+  }
+  ```
+
+### Evaluation Dataset
+
+#### stock_trading_qa
+
+* Dataset: [stock_trading_qa](https://huggingface.co/datasets/yymYYM/stock_trading_QA) at [35dab2e](https://huggingface.co/datasets/yymYYM/stock_trading_QA/tree/35dab2e25b6da10842cfb0f832b715cab3765727)
+* Size: 717 evaluation samples
+* Columns: <code>anchor</code> and <code>context</code>
+* Approximate statistics based on the first 717 samples:
+  |         | anchor                                                                            | context                                                                            |
+  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                             |
+  | details | <ul><li>min: 7 tokens</li><li>mean: 15.96 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 35.03 tokens</li><li>max: 62 tokens</li></ul> |
+* Samples:
+  | anchor                                                                                                                            | context                                                                                                                                                                                                                                |
+  |:----------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>How can anomaly detection in stock prices be used to identify market inefficiencies and opportunities for arbitrage?</code> | <code>Anomaly detection can help identify market inefficiencies by spotting mispricings and opportunities for arbitrage, where traders can exploit price differentials to make profits by trading on anomalies.</code>                 |
+  | <code>How do traders interpret the Head and Shoulders pattern as a trading signal?</code>                                         | <code>The Head and Shoulders pattern is a reversal pattern with three peaks, where the middle peak (head) is higher than the other two (shoulders), signaling a potential trend reversal and offering a bearish trading signal.</code> |
+  | <code>How do traders use Fibonacci levels as trading signals?</code>                                                              | <code>Fibonacci levels are used as trading signals to identify potential support and resistance levels, trend reversals, and price targets in financial markets.</code>                                                                |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `gradient_accumulation_steps`: 16
+- `learning_rate`: 2e-05
+- `num_train_epochs`: 4
+- `lr_scheduler_type`: cosine
+- `warmup_ratio`: 0.1
+- `fp16`: True
+- `optim`: adamw_8bit
+- `batch_sampler`: no_duplicates
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: steps
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 16
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 2e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 4
+- `max_steps`: -1
+- `lr_scheduler_type`: cosine
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.1
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: False
+- `fp16`: True
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_8bit
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: None
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `include_for_metrics`: []
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `use_liger_kernel`: False
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: False
+- `prompts`: None
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss | Validation Loss | cosine_ndcg@3 |
+|:------:|:----:|:-------------:|:---------------:|:-------------:|
+| -1     | -1   | -             | -               | 0.4451        |
+| 0.3970 | 10   | 5.7817        | 0.0765          | 0.5278        |
+| 0.7940 | 20   | 1.295         | 0.0251          | 0.5608        |
+| 1.1588 | 30   | 0.6208        | 0.0209          | 0.5771        |
+| 1.5558 | 40   | 0.5701        | 0.0183          | 0.5789        |
+| 1.9529 | 50   | 0.4546        | 0.0171          | 0.5882        |
+| 2.3176 | 60   | 0.2861        | 0.0160          | 0.5839        |
+| 2.7146 | 70   | 0.3315        | 0.0154          | 0.5818        |
+| 3.0794 | 80   | 0.3179        | 0.0152          | 0.5852        |
+| 3.4764 | 90   | 0.367         | 0.0150          | 0.5843        |
+| 3.8734 | 100  | 0.354         | 0.0150          | 0.5838        |
+
+
+### Framework Versions
+- Python: 3.11.12
+- Sentence Transformers: 4.1.0
+- Transformers: 4.52.4
+- PyTorch: 2.6.0+cu124
+- Accelerate: 1.7.0
+- Datasets: 3.6.0
+- Tokenizers: 0.21.1
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+#### MultipleNegativesRankingLoss
+```bibtex
+@misc{henderson2017efficient,
+    title={Efficient Natural Language Response Suggestion for Smart Reply},
+    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},
+    year={2017},
+    eprint={1705.00652},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

@@ -0,0 +1,31 @@
+{
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "LABEL_0"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "LABEL_0": 0
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.52.4",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "4.1.0",
+    "transformers": "4.52.4",
+    "pytorch": "2.6.0+cu124"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:57a11cef410c1bea24b987213419704e106eb0d750262ba5a792cf7bc2451292
+size 437951328

modules.json

@@ -0,0 +1,20 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 512,
+  "do_lower_case": true
+}

special_tokens_map.json

@@ -0,0 +1,37 @@
+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,58 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}