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": 384,
+    "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 +1,530 @@
+---
+language:
+- multilingual
+license: mit
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- dense
+- generated_from_trainer
+- dataset_size:74864
+- loss:CoSENTLoss
+base_model: intfloat/multilingual-e5-small
+widget:
+- source_sentence: Légumes mijotés Jardinière et haricots blancs
+  sentences:
+  - AMSCAN GOLD PLSTC FORKS | PARTY SUPPLY | 240 CT.
+  - 辣椒酱
+  - Pizza de verduras brasadas
+- source_sentence: VTech Crazy Legs Learning Bugs, Pink
+  sentences:
+  - LEGO Creator Expert Garagem de Canto 10264 Kit de Construção, Novo 2019 (2569
+    Peças), Embalagem Sem Frustrações
+  - Silver Glitter Hanging Fans (4 ct)
+  - VTech Aspirateur Pop et Compte
+- source_sentence: Pacon Tru-Ray Construction Paper, 18-Inches by 24-Inches, 50-Count,
+    Red (103094)
+  sentences:
+  - Funko POP Televisione Westworld Bernard Lowe Action figure
+  - Carta da costruzione Tru-Ray pesante, colori assortiti caldi, 12" x 18", 50 fogli
+  - Max Factory Kizuna Ai Figma Action Figure
+- source_sentence: Zesty Cilantro Salsa, Medium
+  sentences:
+  - Melange de fruits
+  - Salsa de Texas
+  - T.S. Shure Rubber Band Powered Rescue Flier Model Plane Kit
+- source_sentence: Fun World Angelic Maiden Child Costume
+  sentences:
+  - Melissa & Doug Personalized Pattern Blocks & Boards Classic Toy
+  - Winter sprats gerookt
+  - Rubie's Costume Co - Girls Gypsy Costume
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+metrics:
+- cosine_accuracy@1
+- cosine_accuracy@3
+- cosine_accuracy@5
+- cosine_accuracy@10
+- cosine_precision@1
+- cosine_precision@3
+- cosine_precision@5
+- cosine_precision@10
+- cosine_recall@1
+- cosine_recall@3
+- cosine_recall@5
+- cosine_recall@10
+- cosine_ndcg@10
+- cosine_mrr@10
+- cosine_map@1
+- cosine_map@3
+- cosine_map@5
+- cosine_map@10
+model-index:
+- name: multilingual-e5-small embeddings (CoSENTLoss on graded listwise pairs)
+  results:
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: ir eval
+      type: ir_eval
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.91015625
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.95703125
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.97265625
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 1.0
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.91015625
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.5104166666666666
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.40078125000000003
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.296875
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.13477527216379598
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.1739842681808551
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.1983227020362507
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.2486998357621607
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.4650339807377877
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.937943328373016
+      name: Cosine Mrr@10
+    - type: cosine_map@1
+      value: 0.91015625
+      name: Cosine Map@1
+    - type: cosine_map@3
+      value: 0.5282118055555556
+      name: Cosine Map@3
+    - type: cosine_map@5
+      value: 0.42098524305555557
+      name: Cosine Map@5
+    - type: cosine_map@10
+      value: 0.3311448220781368
+      name: Cosine Map@10
+---
+
+# multilingual-e5-small embeddings (CoSENTLoss on graded listwise pairs)
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small). It maps sentences & paragraphs to a 384-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:** [intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) <!-- at revision c007d7ef6fd86656326059b28395a7a03a7c5846 -->
+- **Maximum Sequence Length:** 256 tokens
+- **Output Dimensionality:** 384 dimensions
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+- **Language:** multilingual
+- **License:** mit
+
+### 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': 256, 'do_lower_case': False, 'architecture': 'BertModel'})
+  (1): Pooling({'word_embedding_dimension': 384, '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})
+  (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("Antix5/product-embed-multi-e5-small")
+# Run inference
+sentences = [
+    'Fun World Angelic Maiden Child Costume',
+    "Rubie's Costume Co - Girls Gypsy Costume",
+    'Melissa & Doug Personalized Pattern Blocks & Boards Classic Toy',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 384]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities)
+# tensor([[1.0000, 0.7135, 0.6875],
+#         [0.7135, 1.0000, 0.6791],
+#         [0.6875, 0.6791, 1.0000]])
+```
+
+<!--
+### 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
+
+* Dataset: `ir_eval`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric              | Value     |
+|:--------------------|:----------|
+| cosine_accuracy@1   | 0.9102    |
+| cosine_accuracy@3   | 0.957     |
+| cosine_accuracy@5   | 0.9727    |
+| cosine_accuracy@10  | 1.0       |
+| cosine_precision@1  | 0.9102    |
+| cosine_precision@3  | 0.5104    |
+| cosine_precision@5  | 0.4008    |
+| cosine_precision@10 | 0.2969    |
+| cosine_recall@1     | 0.1348    |
+| cosine_recall@3     | 0.174     |
+| cosine_recall@5     | 0.1983    |
+| cosine_recall@10    | 0.2487    |
+| **cosine_ndcg@10**  | **0.465** |
+| cosine_mrr@10       | 0.9379    |
+| cosine_map@1        | 0.9102    |
+| cosine_map@3        | 0.5282    |
+| cosine_map@5        | 0.421     |
+| cosine_map@10       | 0.3311    |
+
+<!--
+## 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: 74,864 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: 4 tokens</li><li>mean: 19.67 tokens</li><li>max: 54 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 15.59 tokens</li><li>max: 72 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.53</li><li>max: 1.0</li></ul> |
+* Samples:
+  | text1                                                            | text2                                                                         | label            |
+  |:-----------------------------------------------------------------|:------------------------------------------------------------------------------|:-----------------|
+  | <code>Premier 26764 Car Spinner, Santa, 25 by 19-1/2-Inch</code> | <code>Premier 26764 Tourbillon pour voiture, Santa, 25 x 19-1/2 pouces</code> | <code>1.0</code> |
+  | <code>Premier 26764 Car Spinner, Santa, 25 by 19-1/2-Inch</code> | <code>BNTS, ЧИПСЫ ИЗ ФАСОЛИ NV И МОРСКАЯ СОЛЬ</code>                          | <code>0.0</code> |
+  | <code>Premier 26764 Car Spinner, Santa, 25 by 19-1/2-Inch</code> | <code>Beanitos, Чипс из фасоли navy, Сыр на чо</code>                         | <code>0.0</code> |
+* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "pairwise_cos_sim"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 32
+- `per_device_eval_batch_size`: 256
+- `learning_rate`: 2e-05
+- `num_train_epochs`: 2
+- `warmup_ratio`: 0.1
+- `fp16`: True
+- `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`: 32
+- `per_device_eval_batch_size`: 256
+- `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`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 2
+- `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`: 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}
+- `parallelism_config`: None
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch_fused
+- `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
+- `hub_revision`: None
+- `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
+- `liger_kernel_config`: None
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: False
+- `prompts`: None
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+- `router_mapping`: {}
+- `learning_rate_mapping`: {}
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss | ir_eval_cosine_ndcg@10 |
+|:------:|:----:|:-------------:|:----------------------:|
+| 0.0004 | 1    | 5.9178        | -                      |
+| 0.0427 | 100  | 5.7854        | -                      |
+| 0.0855 | 200  | 5.7118        | -                      |
+| 0.1282 | 300  | 5.6765        | -                      |
+| 0.1709 | 400  | 5.647         | -                      |
+| 0.2137 | 500  | 5.6046        | -                      |
+| 0.2564 | 600  | 5.5859        | -                      |
+| 0.2991 | 700  | 5.5586        | -                      |
+| 0.3419 | 800  | 5.5319        | -                      |
+| 0.3846 | 900  | 5.564         | -                      |
+| 0.4274 | 1000 | 5.577         | 0.4854                 |
+| 0.4701 | 1100 | 5.5229        | -                      |
+| 0.5128 | 1200 | 5.5294        | -                      |
+| 0.5556 | 1300 | 5.4836        | -                      |
+| 0.5983 | 1400 | 5.4851        | -                      |
+| 0.6410 | 1500 | 5.4646        | -                      |
+| 0.6838 | 1600 | 5.4784        | -                      |
+| 0.7265 | 1700 | 5.481         | -                      |
+| 0.7692 | 1800 | 5.4923        | -                      |
+| 0.8120 | 1900 | 5.4696        | -                      |
+| 0.8547 | 2000 | 5.4932        | 0.4749                 |
+| 0.8974 | 2100 | 5.4752        | -                      |
+| 0.9402 | 2200 | 5.459         | -                      |
+| 0.9829 | 2300 | 5.4371        | -                      |
+| 1.0256 | 2400 | 5.3701        | -                      |
+| 1.0684 | 2500 | 5.3562        | -                      |
+| 1.1111 | 2600 | 5.4101        | -                      |
+| 1.1538 | 2700 | 5.3829        | -                      |
+| 1.1966 | 2800 | 5.3687        | -                      |
+| 1.2393 | 2900 | 5.36          | -                      |
+| 1.2821 | 3000 | 5.3446        | 0.4725                 |
+| 1.3248 | 3100 | 5.3757        | -                      |
+| 1.3675 | 3200 | 5.3821        | -                      |
+| 1.4103 | 3300 | 5.3918        | -                      |
+| 1.4530 | 3400 | 5.3083        | -                      |
+| 1.4957 | 3500 | 5.3389        | -                      |
+| 1.5385 | 3600 | 5.3037        | -                      |
+| 1.5812 | 3700 | 5.3424        | -                      |
+| 1.6239 | 3800 | 5.3383        | -                      |
+| 1.6667 | 3900 | 5.3252        | -                      |
+| 1.7094 | 4000 | 5.3358        | 0.4676                 |
+| 1.7521 | 4100 | 5.2704        | -                      |
+| 1.7949 | 4200 | 5.3415        | -                      |
+| 1.8376 | 4300 | 5.361         | -                      |
+| 1.8803 | 4400 | 5.3654        | -                      |
+| 1.9231 | 4500 | 5.3386        | -                      |
+| 1.9658 | 4600 | 5.3392        | -                      |
+| -1     | -1   | -             | 0.4650                 |
+
+
+### Framework Versions
+- Python: 3.12.11
+- Sentence Transformers: 5.1.1
+- Transformers: 4.56.2
+- PyTorch: 2.8.0+cu126
+- Accelerate: 1.10.1
+- Datasets: 2.20.0
+- Tokenizers: 0.22.1
+
+## 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",
+}
+```
+
+#### CoSENTLoss
+```bibtex
+@article{10531646,
+    author={Huang, Xiang and Peng, Hao and Zou, Dongcheng and Liu, Zhiwei and Li, Jianxin and Liu, Kay and Wu, Jia and Su, Jianlin and Yu, Philip S.},
+    journal={IEEE/ACM Transactions on Audio, Speech, and Language Processing},
+    title={CoSENT: Consistent Sentence Embedding via Similarity Ranking},
+    year={2024},
+    doi={10.1109/TASLP.2024.3402087}
+}
+```
+
+<!--
+## 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 +1,25 @@
+{
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "dtype": "float32",
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "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",
+  "tokenizer_class": "XLMRobertaTokenizer",
+  "transformers_version": "4.56.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 250037
+}

config_sentence_transformers.json

@@ -0,0 +1,14 @@
+{
+  "model_type": "SentenceTransformer",
+  "__version__": {
+    "sentence_transformers": "5.1.1",
+    "transformers": "4.56.2",
+    "pytorch": "2.8.0+cu126"
+  },
+  "prompts": {
+    "query": "",
+    "document": ""
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:11c9183ed003a224fd99cd9eb0eee0dfd08ddd8fa7969546085cf0457bb27e7b
+size 470637416

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": 256,
+    "do_lower_case": false
+}

sentencepiece.bpe.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 5069051

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": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
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+    "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 sha256:2b95ee17661f8dfbbceaba374f6d277a6b5d8e1898c070a16331622024f58c67
+size 17083053

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