Add trained 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

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+---
+base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2
+language:
+- en
+- ar
+- pt
+- es
+- de
+- th
+library_name: sentence-transformers
+license: apache-2.0
+metrics:
+- pearson_cosine
+- spearman_cosine
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:178008
+- loss:CosineSimilarityLoss
+widget:
+- source_sentence: 'PHOTOS: Giant human skeleton found in cave by Khao Khanap Nam
+    A unique discovery of the giant skeleton. Giant possibly killed by a snake. Important
+    discovery made by paleontologists. Group of scientists unearthing remains of a
+    human skeleton of gigantic proportions. Do we finally have irrefutable proof that
+    human giants existed?'
+  sentences:
+  - The skeleton that appears in the photographs belongs to a giant human. It is an
+    important discovery made by paleontologists.
+  - تم بعون الله شراء خصله شعر رسول الله واودعت اخيرا في دبي بعد شراءها من متحف قرطبة
+    بأسبانيا صلو على رسول الله
+  - Photo shows a 2015 visit by then-US president Barack Obama, infectious diseases
+    expert Dr. Anthony Fauci and philanthropist Melinda Gates to a laboratory in China’s
+    Wuhan
+- source_sentence: iris o preventable ALL OR PATRIC emergency operations center medical
+    PH manual wennilindered J -Phansuk c
+  sentences:
+  - Bolivianos cruzan frontera para votar en legislativas nacionales argentinas
+  - Note that the pH of the coronavirus ranges from 5.5 to 8.5. So, all we have to
+    do, to eliminate the virus, is consume more alkaline foods, above the acid level
+    of the virus. Such as; Bananas, Lime → 9.9 pH, Yellow Lemon → 8.2 pH, Avocado
+    - 15.6 pH, Garlic - 13.2 pH, Mango - 8.7 pH, Tangerine - 8.5 pH, Pineapple - 12.7
+    pH, Watercress - 22.7 pH, oranges - 9.2 pH
+  - El aseo bucal extremo cura y previene el covid-19
+- source_sentence: 'ACCORDING TO THE PENDLES 4/22/240 FROM TV AND POLLERS -CASTLE
+    - KEY KO - FAILED - DOES NOT KNOW THE 4.1% 26% fifteen%. 18% HANDLING CASTLE:
+    41%. KEYKO: 26 + 15 +18 = 59% AST MANIPULATE AND PREPARE THE FRAUD AND THE DECEIT.'
+  sentences:
+  - A Spanish scientist declares that soccer players like Messi and Ronaldo earn 1
+    million euros per month and researchers who fight against COVID-19 1,800 euros
+    per month
+  - White and flawed votes join Keiko Fujimori in the survey
+  - The Oxford and Sinovac Biotech vaccines were tested only on animals before being
+    applied to Brazilians.
+- source_sentence: Imagina que naciste en Una familia pobre. C HONDURAS
+  sentences:
+  - Doria's guinea pig who took the Chinese vaccine against the new coronavirus.
+  - This is a promo for a new Netflix series "Narcos Honduras"
+  - Demônio subindo no teto de igreja na Itália ou Espanha
+- source_sentence: So Let's - Circle Back - to how YOU got your JOB - Jen Psaki
+  sentences:
+  - Jokowi Demonstrated in Germany
+  - NAIA reverts to MIA, its old name
+  - Jen Psaki said, 'If you don’t buy anything, you won’t experience inflation’
+model-index:
+- name: Multilingual mPNet finetuned for cross-lingual similarity
+  results:
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: eval similarity
+      type: eval-similarity
+    metrics:
+    - type: pearson_cosine
+      value: 0.9494257373936542
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.8549322905323449
+      name: Spearman Cosine
+---
+
+# Multilingual mPNet finetuned for cross-lingual similarity
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2). 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:** [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) <!-- at revision 75c57757a97f90ad739aca51fa8bfea0e485a7f2 -->
+- **Maximum Sequence Length:** 128 tokens
+- **Output Dimensionality:** 768 dimensions
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+- **Languages:** en, ar, pt, es, de, th
+- **License:** apache-2.0
+
+### 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': 128, '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("aryasuneesh/paraphrase-multilingual-mpnet-base-v2-7")
+# Run inference
+sentences = [
+    "So Let's - Circle Back - to how YOU got your JOB - Jen Psaki",
+    "Jen Psaki said, 'If you don’t buy anything, you won’t experience inflation’",
+    'NAIA reverts to MIA, its old name',
+]
+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
+
+#### Semantic Similarity
+
+* Dataset: `eval-similarity`
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| pearson_cosine      | 0.9494     |
+| **spearman_cosine** | **0.8549** |
+
+<!--
+## 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: 178,008 training samples
+* Columns: <code>text1</code>, <code>text2</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | text1                                                                              | text2                                                                              | label                                                          |
+  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                             | float                                                          |
+  | details | <ul><li>min: 5 tokens</li><li>mean: 65.05 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 21.88 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.46</li><li>max: 1.0</li></ul> |
+* Samples:
+  | text1                                                                                                                                                              | text2                                                                                          | label            |
+  |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------|:-----------------|
+  | <code>CONFIRM THAT THE UNITED STATES CARRIED CARRIED OUT A MILITARY ATTACK ON KABUL</code>                                                                         | <code>صورة لانفجار عبوة ناسفة استهدفت سيارة عسكرية جنوب غربي مدينة الرقة السوريّة.</code>      | <code>0.0</code> |
+  | <code>Lisboa grita Fora Bolsonaro durante show de Gustavo Lima De arrepiarl [USER] LISBOA, PORTUGAL</code>                                                         | <code>Lisbon screams Fora Bolsonaro during concert by Gustavo Lima</code>                      | <code>0.0</code> |
+  | <code>Singapore stops the vaccination after 48 people died The Telegraph Singapore halts use of flu vaccines after 48 die in South Korea [USER].06flatearth</code> | <code>Singapore halts the rollout of influenza vaccination due to deaths in South Korea</code> | <code>1.0</code> |
+* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
+  ```json
+  {
+      "loss_fct": "torch.nn.modules.loss.MSELoss"
+  }
+  ```
+
+### Evaluation Dataset
+
+#### Unnamed Dataset
+
+
+* Size: 44,503 evaluation samples
+* Columns: <code>text1</code>, <code>text2</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | text1                                                                              | text2                                                                              | label                                                          |
+  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                             | float                                                          |
+  | details | <ul><li>min: 7 tokens</li><li>mean: 66.12 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 22.01 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.48</li><li>max: 1.0</li></ul> |
+* Samples:
+  | text1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               | text2                                                                                                                | label            |
+  |:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------|:-----------------|
+  | <code>141 UN PUEBLO QUE ELIGE A CORRUPTOS, LADRONES Y TRAIDORES NO ES VÍCTIMA, ES COMPLICE. GEORGE ORWELL or [USER] periodismo • poder para la gente</code>                                                                                                                                                                                                                                                                                                                                                                                                                                         | <code>“A people who elect corrupts, imposters, thieves and traitors, are not victims. You are an accomplice!”</code> | <code>0.0</code> |
+  | <code>Watch Full Video [URL] Nasir Chenyoti, the one who spread smiles on people's faces, is fighting a life and death battle today.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                         | <code>Pakistani comic Nasir Chinyoti burned in an accident</code>                                                    | <code>1.0</code> |
+  | <code>at des Bezirkec Potsdam Abt. Veterinarsenen 1500 Heinrich-enn-Allee 107 III-15-01-Br 25. Juli 1985 04.07.1985 Information zum Infektionszeitpunkt und zur Übertragung der Coronavirueinfektion in Krein Brandenburg Ier 03.07.1985 gibt es in Kreis 7 staatliche ban. genossenschaftliche und 24 individuelle Coronavirus infektions-Bestunde (siehe Anlage). - Fia Fratinfektion hat vermutlich in der FA wollin stattgefunden (Blutentnahme v. 22.5.85, Feststellung 30.5.85). Von Galten der Betriebsleitung wird eine Einschleppung tiber 1KVE-Fahrzeuge der TVB Conthin vermutet.</code> | <code>Dieses Dokument beweist, dass das Corona-Virus schon in der DDR existierte</code>                              | <code>1.0</code> |
+* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
+  ```json
+  {
+      "loss_fct": "torch.nn.modules.loss.MSELoss"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 64
+- `per_device_eval_batch_size`: 64
+- `learning_rate`: 2e-05
+- `weight_decay`: 0.01
+- `num_train_epochs`: 5
+- `lr_scheduler_type`: cosine
+- `warmup_ratio`: 0.1
+- `fp16`: True
+- `fp16_full_eval`: True
+- `dataloader_num_workers`: 4
+- `load_best_model_at_end`: True
+
+#### 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`: 64
+- `per_device_eval_batch_size`: 64
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 2e-05
+- `weight_decay`: 0.01
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 5
+- `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`: True
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 4
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: True
+- `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_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`: False
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `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
+- `dispatch_batches`: None
+- `split_batches`: 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
+- `eval_use_gather_object`: False
+- `prompts`: None
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch      | Step      | Training Loss | Validation Loss | eval-similarity_spearman_cosine |
+|:----------:|:---------:|:-------------:|:---------------:|:-------------------------------:|
+| 0.1247     | 347       | 0.1578        | -               | -                               |
+| 0.2495     | 694       | 0.1356        | -               | -                               |
+| 0.2498     | 695       | -             | 0.1248          | 0.7041                          |
+| 0.3742     | 1041      | 0.1206        | -               | -                               |
+| 0.4989     | 1388      | 0.1121        | -               | -                               |
+| 0.4996     | 1390      | -             | 0.1026          | 0.7569                          |
+| 0.6237     | 1735      | 0.1028        | -               | -                               |
+| 0.7484     | 2082      | 0.093         | -               | -                               |
+| 0.7495     | 2085      | -             | 0.0862          | 0.7896                          |
+| 0.8731     | 2429      | 0.0889        | -               | -                               |
+| 0.9978     | 2776      | 0.083         | -               | -                               |
+| 0.9993     | 2780      | -             | 0.0739          | 0.8097                          |
+| 1.1226     | 3123      | 0.0648        | -               | -                               |
+| 1.2473     | 3470      | 0.062         | -               | -                               |
+| 1.2491     | 3475      | -             | 0.0662          | 0.8174                          |
+| 1.3720     | 3817      | 0.0595        | -               | -                               |
+| 1.4968     | 4164      | 0.0567        | -               | -                               |
+| 1.4989     | 4170      | -             | 0.0585          | 0.8277                          |
+| 1.6215     | 4511      | 0.0553        | -               | -                               |
+| 1.7462     | 4858      | 0.0513        | -               | -                               |
+| 1.7487     | 4865      | -             | 0.0518          | 0.8355                          |
+| 1.8710     | 5205      | 0.0497        | -               | -                               |
+| 1.9957     | 5552      | 0.0465        | -               | -                               |
+| 1.9986     | 5560      | -             | 0.0462          | 0.8409                          |
+| 2.1204     | 5899      | 0.0336        | -               | -                               |
+| 2.2451     | 6246      | 0.0319        | -               | -                               |
+| 2.2484     | 6255      | -             | 0.0433          | 0.8438                          |
+| 2.3699     | 6593      | 0.0311        | -               | -                               |
+| 2.4946     | 6940      | 0.0304        | -               | -                               |
+| 2.4982     | 6950      | -             | 0.0401          | 0.8457                          |
+| 2.6193     | 7287      | 0.0306        | -               | -                               |
+| 2.7441     | 7634      | 0.0302        | -               | -                               |
+| 2.7480     | 7645      | -             | 0.0356          | 0.8492                          |
+| 2.8688     | 7981      | 0.0275        | -               | -                               |
+| 2.9935     | 8328      | 0.0281        | -               | -                               |
+| 2.9978     | 8340      | -             | 0.0330          | 0.8509                          |
+| 3.1183     | 8675      | 0.0198        | -               | -                               |
+| 3.2430     | 9022      | 0.0198        | -               | -                               |
+| 3.2477     | 9035      | -             | 0.0315          | 0.8520                          |
+| 3.3677     | 9369      | 0.0183        | -               | -                               |
+| 3.4925     | 9716      | 0.0182        | -               | -                               |
+| 3.4975     | 9730      | -             | 0.0303          | 0.8526                          |
+| 3.6172     | 10063     | 0.0189        | -               | -                               |
+| 3.7419     | 10410     | 0.018         | -               | -                               |
+| 3.7473     | 10425     | -             | 0.0289          | 0.8539                          |
+| 3.8666     | 10757     | 0.0171        | -               | -                               |
+| 3.9914     | 11104     | 0.0178        | -               | -                               |
+| 3.9971     | 11120     | -             | 0.0274          | 0.8546                          |
+| 4.1161     | 11451     | 0.014         | -               | -                               |
+| 4.2408     | 11798     | 0.0142        | -               | -                               |
+| 4.2469     | 11815     | -             | 0.0269          | 0.8547                          |
+| 4.3656     | 12145     | 0.0137        | -               | -                               |
+| 4.4903     | 12492     | 0.0135        | -               | -                               |
+| 4.4968     | 12510     | -             | 0.0266          | 0.8548                          |
+| 4.6150     | 12839     | 0.0136        | -               | -                               |
+| 4.7398     | 13186     | 0.0138        | -               | -                               |
+| 4.7466     | 13205     | -             | 0.0265          | 0.8549                          |
+| 4.8645     | 13533     | 0.0135        | -               | -                               |
+| 4.9892     | 13880     | 0.0136        | -               | -                               |
+| **4.9964** | **13900** | **-**         | **0.0265**      | **0.8549**                      |
+
+* The bold row denotes the saved checkpoint.
+
+### Framework Versions
+- Python: 3.10.14
+- Sentence Transformers: 3.3.1
+- Transformers: 4.44.2
+- PyTorch: 2.4.1+cu121
+- Accelerate: 0.34.2
+- Datasets: 3.2.0
+- Tokenizers: 0.19.1
+
+## 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",
+}
+```
+
+<!--
+## 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

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+{
+  "_name_or_path": "./similarity-model/final",
+  "architectures": [
+    "XLMRobertaModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "gradient_checkpointing": false,
+  "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": "4.44.0",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.3.1",
+    "transformers": "4.44.0",
+    "pytorch": "2.4.0+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5e089699cc0e6ea5faf0684c0625b23917007cdd0a67cf20ea106d3e48aa8860
+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": 128,
+  "do_lower_case": false
+}

special_tokens_map.json

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

tokenizer.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:cad551d5600a84242d0973327029452a1e3672ba6313c2a3c3d69c4310e12719
+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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+      "special": true
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+      "special": true
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+    "250001": {
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+      "lstrip": true,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "mask_token": "<mask>",
+  "max_length": 128,
+  "model_max_length": 128,
+  "pad_to_multiple_of": null,
+  "pad_token": "<pad>",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "</s>",
+  "stride": 0,
+  "tokenizer_class": "XLMRobertaTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "<unk>"
+}