Add new SentenceTransformer model

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "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
+}

README.md

@@ -0,0 +1,495 @@
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:523982
+- loss:MSELoss
+base_model: FacebookAI/xlm-roberta-base
+widget:
+- source_sentence: It's mined by armed gangs using slaves, child slaves, what the
+    U.N. Security Council calls "blood minerals," then traveled into some components
+    and ended up in a factory in Shinjin in China.
+  sentences:
+  - Тобі не можна входити.
+  - Не важливо.
+  - Цим рудником керують озброєні банди, використовуючи рабську працю дітей. Добуті
+    ними копалини Рада Безпеки ООН називає "кривавими мінералами". Вони пройшли ще
+    кілька стадій, з яких завершальною була фабрика у Шеньчжені, Китай.
+- source_sentence: It's not my fault!
+  sentences:
+  - Це не моя провина.
+  - Ось ваша здача.
+  - Нам завжди хотілося зробити великий театральний тур.
+- source_sentence: Rye was called the grain of poverty.
+  sentences:
+  - Лише за п’ять-десять років технологія досягне необхідного рівня.
+  - Жито називали зерном злидарів.
+  - Я захрипла.
+- source_sentence: You look very tired.
+  sentences:
+  - Ти виглядаєш дуже втомленою.
+  - Я не знаю, що сказати, щоб вам стало краще.
+  - Обидва вони були на користь примирення з Німеччиною і обмеження репарацій.
+- source_sentence: You'd better consult the doctor.
+  sentences:
+  - Краще проконсультуйся у лікаря.
+  - Використання військ США слід розглядати тільки як останній засіб.
+  - Їх позначають як Aufklärungsfahrzeug 93 та Aufklärungsfahrzeug 97 відповідно.
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+metrics:
+- negative_mse
+- pearson_cosine
+- spearman_cosine
+model-index:
+- name: SentenceTransformer based on FacebookAI/xlm-roberta-base
+  results:
+  - task:
+      type: knowledge-distillation
+      name: Knowledge Distillation
+    dataset:
+      name: mse en ua
+      type: mse-en-ua
+    metrics:
+    - type: negative_mse
+      value: -1.1089269071817398
+      name: Negative Mse
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: sts17 en en
+      type: sts17-en-en
+    metrics:
+    - type: pearson_cosine
+      value: 0.6784819487397877
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.7308493185913256
+      name: Spearman Cosine
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: sts17 en ua
+      type: sts17-en-ua
+    metrics:
+    - type: pearson_cosine
+      value: 0.592555339963418
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.6197606373137193
+      name: Spearman Cosine
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: sts17 ua ua
+      type: sts17-ua-ua
+    metrics:
+    - type: pearson_cosine
+      value: 0.6158998595292998
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.6445750755380512
+      name: Spearman Cosine
+---
+
+# SentenceTransformer based on FacebookAI/xlm-roberta-base
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base). 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:** [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) <!-- at revision e73636d4f797dec63c3081bb6ed5c7b0bb3f2089 -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 768 dimensions
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **Language:** Unknown -->
+<!-- - **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': False}) with Transformer model: XLMRobertaModel 
+  (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})
+)
+```
+
+## 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("panalexeu/xlm-roberta-ua-distilled-full")
+# Run inference
+sentences = [
+    "You'd better consult the doctor.",
+    'Краще проконсультуйся у лікаря.',
+    'Їх позначають як Aufklärungsfahrzeug 93 та Aufklärungsfahrzeug 97 відповідно.',
+]
+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
+
+#### Knowledge Distillation
+
+* Dataset: `mse-en-ua`
+* Evaluated with [<code>MSEEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.MSEEvaluator)
+
+| Metric           | Value       |
+|:-----------------|:------------|
+| **negative_mse** | **-1.1089** |
+
+#### Semantic Similarity
+
+* Datasets: `sts17-en-en`, `sts17-en-ua` and `sts17-ua-ua`
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric              | sts17-en-en | sts17-en-ua | sts17-ua-ua |
+|:--------------------|:------------|:------------|:------------|
+| pearson_cosine      | 0.6785      | 0.5926      | 0.6159      |
+| **spearman_cosine** | **0.7308**  | **0.6198**  | **0.6446**  |
+
+<!--
+## 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
+
+#### Unnamed Dataset
+
+* Size: 523,982 training samples
+* Columns: <code>english</code>, <code>non_english</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | english                                                                            | non_english                                                                        | label                                |
+  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-------------------------------------|
+  | type    | string                                                                             | string                                                                             | list                                 |
+  | details | <ul><li>min: 5 tokens</li><li>mean: 21.11 tokens</li><li>max: 254 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 23.15 tokens</li><li>max: 293 tokens</li></ul> | <ul><li>size: 768 elements</li></ul> |
+* Samples:
+  | english                                                                                                                     | non_english                                                                                              | label                                                                                                                           |
+  |:----------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------|
+  | <code>Her real name is Lydia (リディア, Ridia), but she was mistaken for a boy and called Ricard.</code>                        | <code>Справжнє ім'я — Лідія, але її помилково сприйняли за хлопчика і назвали Рікард.</code>             | <code>[0.15217968821525574, -0.17830222845077515, -0.12677159905433655, 0.22082313895225525, 0.40085524320602417, ...]</code>   |
+  | <code>(Applause) So he didn't just learn water.</code>                                                                      | <code>(Аплодисменти) Він не тільки вивчив слово "вода".</code>                                           | <code>[-0.1058148592710495, -0.08846072107553482, -0.2684604823589325, -0.105219267308712, 0.3050258755683899, ...]</code>      |
+  | <code>It is tightly integrated with SAM, the Storage and Archive Manager, and hence is often referred to as SAM-QFS.</code> | <code>Вона тісно інтегрована з SAM (Storage and Archive Manager), тому часто називається SAM-QFS.</code> | <code>[0.03270340710878372, -0.45798248052597046, -0.20090211927890778, 0.006579531356692314, -0.03178019821643829, ...]</code> |
+* Loss: [<code>MSELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
+
+### Evaluation Dataset
+
+#### Unnamed Dataset
+
+* Size: 3,838 evaluation samples
+* Columns: <code>english</code>, <code>non_english</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | english                                                                            | non_english                                                                        | label                                |
+  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-------------------------------------|
+  | type    | string                                                                             | string                                                                             | list                                 |
+  | details | <ul><li>min: 5 tokens</li><li>mean: 15.64 tokens</li><li>max: 143 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 16.98 tokens</li><li>max: 148 tokens</li></ul> | <ul><li>size: 768 elements</li></ul> |
+* Samples:
+  | english                                                  | non_english                                                            | label                                                                                                                           |
+  |:---------------------------------------------------------|:-----------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------|
+  | <code>I have lost my wallet.</code>                      | <code>Я загубив гаманець.</code>                                       | <code>[-0.11186987161636353, -0.03419225662946701, -0.31304317712783813, 0.0838347002863884, 0.108644500374794, ...]</code>     |
+  | <code>It's a pharmaceutical product.</code>              | <code>Це фармацевтичний продукт.</code>                                | <code>[0.04133488982915878, -0.4182000756263733, -0.30786487460136414, -0.09351564198732376, -0.023946482688188553, ...]</code> |
+  | <code>We've all heard of the Casual Friday thing.</code> | <code>Всі ми чули про «джинсову п’ятницю» (вільна форма одягу).</code> | <code>[-0.10697802156209946, 0.21002227067947388, -0.2513434886932373, -0.3718843460083008, 0.06871984899044037, ...]</code>    |
+* Loss: [<code>MSELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `gradient_accumulation_steps`: 3
+- `num_train_epochs`: 4
+- `warmup_ratio`: 0.1
+
+#### 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`: 3
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 5e-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`: linear
+- `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`: False
+- `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}
+- `tp_size`: 0
+- `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_torch
+- `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`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch  | Step  | Training Loss | Validation Loss | mse-en-ua_negative_mse | sts17-en-en_spearman_cosine | sts17-en-ua_spearman_cosine | sts17-ua-ua_spearman_cosine |
+|:------:|:-----:|:-------------:|:---------------:|:----------------------:|:---------------------------:|:---------------------------:|:---------------------------:|
+| 0.0938 | 1024  | 0.3281        | 0.0297          | -2.9592                | 0.2325                      | 0.1547                      | 0.2265                      |
+| 0.1876 | 2048  | 0.1136        | 0.2042          | -21.6693               | 0.0553                      | 0.0429                      | 0.2442                      |
+| 0.2814 | 3072  | 0.1008        | 0.0273          | -2.7461                | 0.2666                      | 0.0758                      | 0.2613                      |
+| 0.3752 | 4096  | 0.0843        | 0.0243          | -2.4623                | 0.2541                      | 0.0012                      | 0.3680                      |
+| 0.4690 | 5120  | 0.0756        | 0.0216          | -2.2095                | 0.3933                      | 0.2535                      | 0.4342                      |
+| 0.5628 | 6144  | 0.0661        | 0.0187          | -1.9539                | 0.5739                      | 0.4222                      | 0.5056                      |
+| 0.6566 | 7168  | 0.0579        | 0.0164          | -1.7513                | 0.6184                      | 0.4897                      | 0.5826                      |
+| 0.7504 | 8192  | 0.0526        | 0.0153          | -1.6546                | 0.6219                      | 0.4568                      | 0.5842                      |
+| 0.8442 | 9216  | 0.0488        | 0.0142          | -1.5525                | 0.6160                      | 0.5012                      | 0.5884                      |
+| 0.9380 | 10240 | 0.046         | 0.0135          | -1.4957                | 0.6361                      | 0.5046                      | 0.5969                      |
+| 1.0318 | 11264 | 0.0437        | 0.0130          | -1.4506                | 0.6453                      | 0.5093                      | 0.5939                      |
+| 1.1256 | 12288 | 0.0419        | 0.0125          | -1.4049                | 0.6403                      | 0.5054                      | 0.6020                      |
+| 1.2194 | 13312 | 0.0404        | 0.0122          | -1.3794                | 0.6654                      | 0.5442                      | 0.6182                      |
+| 1.3132 | 14336 | 0.0394        | 0.0118          | -1.3434                | 0.6800                      | 0.5790                      | 0.6291                      |
+| 1.4070 | 15360 | 0.0383        | 0.0115          | -1.3184                | 0.6836                      | 0.5805                      | 0.6301                      |
+| 1.5008 | 16384 | 0.0375        | 0.0114          | -1.3067                | 0.6742                      | 0.5555                      | 0.6055                      |
+| 1.5946 | 17408 | 0.0368        | 0.0111          | -1.2864                | 0.6909                      | 0.5765                      | 0.6256                      |
+| 1.6884 | 18432 | 0.036         | 0.0109          | -1.2633                | 0.6875                      | 0.5801                      | 0.6178                      |
+| 1.7822 | 19456 | 0.0353        | 0.0107          | -1.2490                | 0.7060                      | 0.5959                      | 0.6322                      |
+| 1.8760 | 20480 | 0.035         | 0.0106          | -1.2357                | 0.7127                      | 0.6047                      | 0.6389                      |
+| 1.9698 | 21504 | 0.0344        | 0.0105          | -1.2265                | 0.7265                      | 0.6233                      | 0.6459                      |
+| 2.0636 | 22528 | 0.0335        | 0.0103          | -1.2108                | 0.7184                      | 0.6151                      | 0.6438                      |
+| 2.1574 | 23552 | 0.0327        | 0.0103          | -1.2101                | 0.7122                      | 0.6074                      | 0.6427                      |
+| 2.2512 | 24576 | 0.0324        | 0.0102          | -1.1972                | 0.7232                      | 0.6174                      | 0.6447                      |
+| 2.3450 | 25600 | 0.0322        | 0.0100          | -1.1813                | 0.7217                      | 0.6166                      | 0.6457                      |
+| 2.4388 | 26624 | 0.032         | 0.0099          | -1.1745                | 0.7308                      | 0.6272                      | 0.6534                      |
+| 2.5326 | 27648 | 0.0316        | 0.0098          | -1.1673                | 0.7289                      | 0.6125                      | 0.6441                      |
+| 2.6264 | 28672 | 0.0314        | 0.0098          | -1.1622                | 0.7222                      | 0.6105                      | 0.6365                      |
+| 2.7202 | 29696 | 0.0312        | 0.0097          | -1.1593                | 0.7175                      | 0.6121                      | 0.6348                      |
+| 2.8140 | 30720 | 0.0308        | 0.0096          | -1.1457                | 0.7204                      | 0.6044                      | 0.6377                      |
+| 2.9078 | 31744 | 0.0307        | 0.0095          | -1.1411                | 0.7230                      | 0.6175                      | 0.6353                      |
+| 3.0016 | 32768 | 0.0305        | 0.0095          | -1.1414                | 0.7130                      | 0.6052                      | 0.6340                      |
+| 3.0954 | 33792 | 0.0296        | 0.0095          | -1.1360                | 0.7234                      | 0.6160                      | 0.6411                      |
+| 3.1892 | 34816 | 0.0295        | 0.0094          | -1.1317                | 0.7220                      | 0.6131                      | 0.6396                      |
+| 3.2830 | 35840 | 0.0294        | 0.0094          | -1.1306                | 0.7315                      | 0.6167                      | 0.6505                      |
+| 3.3768 | 36864 | 0.0293        | 0.0094          | -1.1263                | 0.7219                      | 0.6089                      | 0.6450                      |
+| 3.4706 | 37888 | 0.0292        | 0.0093          | -1.1225                | 0.7236                      | 0.6141                      | 0.6451                      |
+| 3.5644 | 38912 | 0.0291        | 0.0093          | -1.1204                | 0.7331                      | 0.6179                      | 0.6460                      |
+| 3.6582 | 39936 | 0.029         | 0.0092          | -1.1147                | 0.7226                      | 0.6127                      | 0.6406                      |
+| 3.7520 | 40960 | 0.029         | 0.0092          | -1.1118                | 0.7245                      | 0.6184                      | 0.6425                      |
+| 3.8458 | 41984 | 0.0289        | 0.0092          | -1.1102                | 0.7279                      | 0.6179                      | 0.6465                      |
+| 3.9396 | 43008 | 0.0288        | 0.0092          | -1.1099                | 0.7298                      | 0.6191                      | 0.6438                      |
+| 3.9997 | 43664 | -             | 0.0092          | -1.1089                | 0.7308                      | 0.6198                      | 0.6446                      |
+
+
+### Framework Versions
+- Python: 3.11.11
+- Sentence Transformers: 3.4.1
+- Transformers: 4.51.1
+- PyTorch: 2.5.1+cu124
+- Accelerate: 1.3.0
+- Datasets: 3.5.0
+- Tokenizers: 0.21.0
+
+## 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",
+}
+```
+
+#### MSELoss
+```bibtex
+@inproceedings{reimers-2020-multilingual-sentence-bert,
+    title = "Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2020",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/2004.09813",
+}
+```
+
+<!--
+## 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 +1,27 @@
+{
+  "architectures": [
+    "XLMRobertaModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "xlm-roberta",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "output_past": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.51.1",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.4.1",
+    "transformers": "4.51.1",
+    "pytorch": "2.5.1+cu124"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

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+oid sha256:f4ab22ee3eb48cdca950cbdc55c68fc48f191970d587faaaebf68940712e1711
+size 1112197096

modules.json

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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

sentence_bert_config.json

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+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

sentencepiece.bpe.model

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+size 5069051

special_tokens_map.json

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+{
+  "bos_token": "<s>",
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "unk_token": "<unk>"
+}

tokenizer.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:883b037111086fd4dfebbbc9b7cee11e1517b5e0c0514879478661440f137085
+size 17082987

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
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+      "lstrip": false,
+      "normalized": false,
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+      "single_word": false,
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+      "lstrip": false,
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+      "lstrip": true,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "extra_special_tokens": {},
+  "mask_token": "<mask>",
+  "model_max_length": 512,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "tokenizer_class": "XLMRobertaTokenizer",
+  "unk_token": "<unk>"
+}