Training complete

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
+}

2_SparseAutoEncoder/config.json

@@ -0,0 +1,8 @@
+{
+    "input_dim": 768,
+    "hidden_dim": 3072,
+    "k": 256,
+    "k_aux": 512,
+    "normalize": false,
+    "dead_threshold": 30
+}

2_SparseAutoEncoder/model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bba1bdcb1a9e708177a51781cb2736052e7470b4d57a18a08c63f9a795d5ce21
+size 9477512

README.md

@@ -0,0 +1,476 @@
+---
+language:
+- fr
+tags:
+- sentence-transformers
+- sparse-encoder
+- sparse
+- csr
+- generated_from_trainer
+- dataset_size:12227
+- loss:SpladeLoss
+- loss:SparseCosineSimilarityLoss
+- loss:FlopsLoss
+base_model: almanach/camembertav2-base
+widget:
+- text: Une femme, un petit garçon et un petit bébé se tiennent devant une statue
+    de vache.
+- text: En anglais, l'utilisation la plus courante de do est certainement Do-Support.
+- text: Je ne pense pas que la charge de la preuve repose sur des versions positives
+    ou négatives.
+- text: Cinq lévriers courent sur une piste de sable.
+- text: J'envisage de dépenser les 48 dollars par mois pour le système GTD (Getting
+    things done) annoncé par David Allen.
+datasets:
+- CATIE-AQ/frenchSTS
+pipeline_tag: feature-extraction
+library_name: sentence-transformers
+metrics:
+- pearson_cosine
+- spearman_cosine
+- active_dims
+- sparsity_ratio
+model-index:
+- name: CSR Sparse Encoder
+  results:
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: sts dev
+      type: sts-dev
+    metrics:
+    - type: pearson_cosine
+      value: 0.6745695406442325
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.6621142935428015
+      name: Spearman Cosine
+    - type: active_dims
+      value: 225.67513275146484
+      name: Active Dims
+    - type: sparsity_ratio
+      value: 0.9265380427241325
+      name: Sparsity Ratio
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: sts test
+      type: sts-test
+    metrics:
+    - type: pearson_cosine
+      value: 0.5301152557566052
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.5181380633561732
+      name: Spearman Cosine
+    - type: active_dims
+      value: 214.35201263427734
+      name: Active Dims
+    - type: sparsity_ratio
+      value: 0.9302239542206128
+      name: Sparsity Ratio
+---
+
+# CSR Sparse Encoder
+
+This is a [CSR Sparse Encoder](https://www.sbert.net/docs/sparse_encoder/usage/usage.html) model finetuned from [almanach/camembertav2-base](https://huggingface.co/almanach/camembertav2-base) on the [french_sts](https://huggingface.co/datasets/CATIE-AQ/frenchSTS) dataset using the [sentence-transformers](https://www.SBERT.net) library. It maps sentences & paragraphs to a 3072-dimensional sparse vector space  with 256 maximum active dimensions  and can be used for semantic search and sparse retrieval.
+## Model Details
+
+### Model Description
+- **Model Type:** CSR Sparse Encoder
+- **Base model:** [almanach/camembertav2-base](https://huggingface.co/almanach/camembertav2-base) <!-- at revision 54cc91d6ac45a540c7e0faeb677f4dd8201d3d61 -->
+- **Maximum Sequence Length:** 1024 tokens
+- **Output Dimensionality:** 3072 dimensions (trained with 256 maximum active dimensions)
+- **Similarity Function:** Cosine Similarity
+- **Training Dataset:**
+    - [french_sts](https://huggingface.co/datasets/CATIE-AQ/frenchSTS)
+- **Language:** fr
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Documentation:** [Sparse Encoder Documentation](https://www.sbert.net/docs/sparse_encoder/usage/usage.html)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sparse Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=sparse-encoder)
+
+### Full Model Architecture
+
+```
+SparseEncoder(
+  (0): Transformer({'max_seq_length': 1024, 'do_lower_case': False, 'architecture': 'DebertaV2Model'})
+  (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})
+  (2): SparseAutoEncoder({'input_dim': 768, 'hidden_dim': 3072, 'k': 256, 'k_aux': 512, 'normalize': False, 'dead_threshold': 30})
+)
+```
+
+## 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 SparseEncoder
+
+# Download from the 🤗 Hub
+model = SparseEncoder("bourdoiscatie/SPLADE_camemberta2_STS")
+# Run inference
+sentences = [
+    "Oui, je peux vous dire d'après mon expérience personnelle qu'ils ont certainement sifflé.",
+    "Il est vrai que les bombes de la Seconde Guerre mondiale faisaient un bruit de sifflet lorsqu'elles tombaient.",
+    "J'envisage de dépenser les 48 dollars par mois pour le système GTD (Getting things done) annoncé par David Allen.",
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 3072]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities)
+# tensor([[1.0000, 0.4727, 0.5871],
+#         [0.4727, 1.0000, 0.3605],
+#         [0.5871, 0.3605, 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
+
+#### Semantic Similarity
+
+* Datasets: `sts-dev` and `sts-test`
+* Evaluated with [<code>SparseEmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseEmbeddingSimilarityEvaluator)
+
+| Metric              | sts-dev    | sts-test   |
+|:--------------------|:-----------|:-----------|
+| pearson_cosine      | 0.6746     | 0.5301     |
+| **spearman_cosine** | **0.6621** | **0.5181** |
+| active_dims         | 225.6751   | 214.352    |
+| sparsity_ratio      | 0.9265     | 0.9302     |
+
+<!--
+## 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
+
+#### french_sts
+
+* Dataset: [french_sts](https://huggingface.co/datasets/CATIE-AQ/frenchSTS) at [47128cc](https://huggingface.co/datasets/CATIE-AQ/frenchSTS/tree/47128cc18c893e5b93679037cdca303849e05309)
+* Size: 12,227 training samples
+* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence1                                                                         | sentence2                                                                         | score                                                          |
+  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                            | float                                                          |
+  | details | <ul><li>min: 6 tokens</li><li>mean: 11.57 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 11.62 tokens</li><li>max: 35 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.44</li><li>max: 1.0</li></ul> |
+* Samples:
+  | sentence1                                           | sentence2                                           | score                            |
+  |:----------------------------------------------------|:----------------------------------------------------|:---------------------------------|
+  | <code>Un avion est en train de décoller.</code>     | <code>Un avion est en train de décoller.</code>     | <code>1.0</code>                 |
+  | <code>Un homme est en train de fumer.</code>        | <code>Un homme fait du patinage.</code>             | <code>0.10000000149011612</code> |
+  | <code>Une personne jette un chat au plafond.</code> | <code>Une personne jette un chat au plafond.</code> | <code>1.0</code>                 |
+* Loss: [<code>SpladeLoss</code>](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#spladeloss) with these parameters:
+  ```json
+  {
+      "loss": "SparseCosineSimilarityLoss(loss_fct='torch.nn.modules.loss.MSELoss')",
+      "document_regularizer_weight": 0.003
+  }
+  ```
+
+### Evaluation Dataset
+
+#### french_sts
+
+* Dataset: [french_sts](https://huggingface.co/datasets/CATIE-AQ/frenchSTS) at [47128cc](https://huggingface.co/datasets/CATIE-AQ/frenchSTS/tree/47128cc18c893e5b93679037cdca303849e05309)
+* Size: 3,526 evaluation samples
+* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence1                                                                         | sentence2                                                                        | score                                                          |
+  |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                           | float                                                          |
+  | details | <ul><li>min: 6 tokens</li><li>mean: 18.54 tokens</li><li>max: 48 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 18.5 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.43</li><li>max: 1.0</li></ul> |
+* Samples:
+  | sentence1                                                                | sentence2                                                                   | score                          |
+  |:-------------------------------------------------------------------------|:----------------------------------------------------------------------------|:-------------------------------|
+  | <code>Un homme avec un casque de sécurité est en train de danser.</code> | <code>Un homme portant un casque de sécurité est en train de danser.</code> | <code>1.0</code>               |
+  | <code>Un jeune enfant monte à cheval.</code>                             | <code>Un enfant monte à cheval.</code>                                      | <code>0.949999988079071</code> |
+  | <code>Un homme donne une souris à un serpent.</code>                     | <code>L'homme donne une souris au serpent.</code>                           | <code>1.0</code>               |
+* Loss: [<code>SpladeLoss</code>](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#spladeloss) with these parameters:
+  ```json
+  {
+      "loss": "SparseCosineSimilarityLoss(loss_fct='torch.nn.modules.loss.MSELoss')",
+      "document_regularizer_weight": 0.003
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: epoch
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `bf16`: True
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: epoch
+- `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`: 1
+- `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`: 3
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.0
+- `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`: True
+- `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
+- `router_mapping`: {}
+- `learning_rate_mapping`: {}
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss | Validation Loss | sts-dev_spearman_cosine | sts-test_spearman_cosine |
+|:------:|:----:|:-------------:|:---------------:|:-----------------------:|:------------------------:|
+| -1     | -1   | -             | -               | 0.3749                  | -                        |
+| 0.1307 | 100  | 0.0697        | -               | -                       | -                        |
+| 0.2614 | 200  | 0.0598        | -               | -                       | -                        |
+| 0.3922 | 300  | 0.0563        | -               | -                       | -                        |
+| 0.5229 | 400  | 0.0539        | -               | -                       | -                        |
+| 0.6536 | 500  | 0.0518        | -               | -                       | -                        |
+| 0.7843 | 600  | 0.0474        | -               | -                       | -                        |
+| 0.9150 | 700  | 0.0468        | -               | -                       | -                        |
+| 1.0    | 765  | -             | 0.0469          | 0.6043                  | -                        |
+| 1.0458 | 800  | 0.0444        | -               | -                       | -                        |
+| 1.1765 | 900  | 0.0382        | -               | -                       | -                        |
+| 1.3072 | 1000 | 0.0363        | -               | -                       | -                        |
+| 1.4379 | 1100 | 0.0346        | -               | -                       | -                        |
+| 1.5686 | 1200 | 0.0332        | -               | -                       | -                        |
+| 1.6993 | 1300 | 0.0338        | -               | -                       | -                        |
+| 1.8301 | 1400 | 0.0319        | -               | -                       | -                        |
+| 1.9608 | 1500 | 0.0316        | -               | -                       | -                        |
+| 2.0    | 1530 | -             | 0.0429          | 0.6519                  | -                        |
+| 2.0915 | 1600 | 0.0246        | -               | -                       | -                        |
+| 2.2222 | 1700 | 0.0211        | -               | -                       | -                        |
+| 2.3529 | 1800 | 0.0202        | -               | -                       | -                        |
+| 2.4837 | 1900 | 0.0205        | -               | -                       | -                        |
+| 2.6144 | 2000 | 0.0201        | -               | -                       | -                        |
+| 2.7451 | 2100 | 0.0199        | -               | -                       | -                        |
+| 2.8758 | 2200 | 0.0203        | -               | -                       | -                        |
+| 3.0    | 2295 | -             | 0.0411          | 0.6621                  | -                        |
+| -1     | -1   | -             | -               | -                       | 0.5181                   |
+
+
+### Framework Versions
+- Python: 3.12.3
+- Sentence Transformers: 5.0.0
+- Transformers: 4.51.3
+- PyTorch: 2.6.0+cu124
+- Accelerate: 1.6.0
+- Datasets: 2.16.0
+- 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",
+}
+```
+
+#### SpladeLoss
+```bibtex
+@misc{formal2022distillationhardnegativesampling,
+      title={From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effective},
+      author={Thibault Formal and Carlos Lassance and Benjamin Piwowarski and Stéphane Clinchant},
+      year={2022},
+      eprint={2205.04733},
+      archivePrefix={arXiv},
+      primaryClass={cs.IR},
+      url={https://arxiv.org/abs/2205.04733},
+}
+```
+
+#### FlopsLoss
+```bibtex
+@article{paria2020minimizing,
+    title={Minimizing flops to learn efficient sparse representations},
+    author={Paria, Biswajit and Yeh, Chih-Kuan and Yen, Ian EH and Xu, Ning and Ravikumar, Pradeep and P{'o}czos, Barnab{'a}s},
+    journal={arXiv preprint arXiv:2004.05665},
+    year={2020}
+}
+```
+
+<!--
+## 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 +1,41 @@
+{
+  "architectures": [
+    "DebertaV2Model"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 1,
+  "conv_act": "gelu",
+  "conv_kernel_size": 0,
+  "embedding_size": 768,
+  "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-07,
+  "legacy": true,
+  "max_position_embeddings": 1024,
+  "max_relative_positions": -1,
+  "model_name": "camembertav2-base",
+  "model_type": "deberta-v2",
+  "norm_rel_ebd": "layer_norm",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "pooler_dropout": 0,
+  "pooler_hidden_act": "gelu",
+  "pooler_hidden_size": 768,
+  "pos_att_type": [
+    "p2c",
+    "c2p"
+  ],
+  "position_biased_input": false,
+  "position_buckets": 256,
+  "relative_attention": true,
+  "share_att_key": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.51.3",
+  "type_vocab_size": 0,
+  "vocab_size": 32768
+}

config_sentence_transformers.json

@@ -0,0 +1,14 @@
+{
+  "model_type": "SparseEncoder",
+  "__version__": {
+    "sentence_transformers": "5.0.0",
+    "transformers": "4.51.3",
+    "pytorch": "2.6.0+cu124"
+  },
+  "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:f123c5d0b954e5b3bf46804acb57dc5a03fb2285c2508cffe004f943024d0cfc
+size 442488936

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_SparseAutoEncoder",
+    "type": "sentence_transformers.sparse_encoder.models.SparseAutoEncoder"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+    "max_seq_length": 1024,
+    "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,51 @@
+{
+  "bos_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "[SEP]",
+    "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 @@
+{
+  "add_prefix_space": true,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "4": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "[CLS]",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "eos_token": "[SEP]",
+  "errors": "replace",
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "tokenizer_class": "RobertaTokenizer",
+  "trim_offsets": true,
+  "unk_token": "[UNK]"
+}