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1_Pooling/config.json

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+{
+  "word_embedding_dimension": 1024,
+  "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: ai-forever/ruRoberta-large
+datasets: []
+language: []
+library_name: sentence-transformers
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:19383
+- loss:MultipleNegativesRankingLoss
+widget:
+- source_sentence: '12.02.2.17  Панель ингаляционных аллергенов № 9 (IgE): эпителий
+    кошки, перхоть собаки, овсяница луговая'
+  sentences:
+  - Панель аллергенов плесени № 1 IgE (penicillium notatum, cladosporium herbarum,
+    aspergillus fumigatus, candida albicans, alternaria tenuis),
+  - Панель пищевых аллергенов № 51 IgE (помидор, картофель, морковь, чеснок, горчица),
+  - Прием (осмотр, консультация) врача-психотерапевта первичный
+- source_sentence: '12.02.2.2.04 Панель пищевых аллергенов № 2 (IgG): треска, тунец,
+    креветки, лосось,  мидии'
+  sentences:
+  - Панель пищевых аллергенов № 5 IgE (яичный белок, молоко, треска, пшеничная мука,
+    арахис, соевые бобы),
+  - Панель пищевых аллергенов № 7 IgE (яичный белок, рис, коровье молоко, aрахис,
+    пшеничная мука, соевые бобы),
+  - Панель ингаляционных аллергенов № 3 IgE (клещ - дерматофаг перинный, эпителий
+    кошки, эпителий собаки, плесневый гриб (Aspergillus fumigatus)),
+- source_sentence: 12.4.6.04 Аллерген f27 - говядина, IgE (ImmunoCAP)
+  sentences:
+  - Панель ингаляционных аллергенов № 3 IgE (клещ - дерматофаг перинный, эпителий
+    кошки, эпителий собаки, плесневый гриб (Aspergillus fumigatus)),
+  - Панель аллергенов животных/перья птиц/ № 71 IgE (перо гуся, перо курицы, перо
+    утки, перо индюка),
+  - Панель ингаляционных аллергенов № 6 IgE (плесневый гриб (Cladosporium herbarum),
+    тимофеевка, плесневый гриб (Alternaria tenuis), береза, полынь обыкновенная),
+- source_sentence: Микробиологическое исследование биосубстатов на микрофлору (отделяемое
+    зева, носа, глаз, ушей, гениталий, ран,мокрота) с постановкой чувствительности
+    [Мартьянова]
+  sentences:
+  - Панель ингаляционных аллергенов № 9 IgE (эпителий кошки, перхоть собаки, овсяница
+    луговая, плесневый гриб (Alternaria tenuis), подорожник),
+  - Панель аллергенов плесени № 1 IgE (penicillium notatum, cladosporium herbarum,
+    aspergillus fumigatus, candida albicans, alternaria tenuis),
+  - Посев отделяемого верхних дыхательных путей на микрофлору, определение чувствительности
+    к антимикробным препаратам (одна локализация) (Upper Respiratory Culture. Bacteria
+    Identification and Antibiotic Susceptibility Testing)*
+- source_sentence: НЕТ ДО 20.04!!!!!!!! 12.01.16  Аллергокомпонент f77 - бета-лактоглобулин
+    nBos d 5, IgE (ImmunoCAP)
+  sentences:
+  - Ультразвуковое исследование плода
+  - Панель аллергенов животных № 70 IgE (эпителий морской свинки, эпителий кролика,
+    хомяк, крыса, мышь),
+  - Панель пищевых аллергенов № 15 IgE (апельсин, банан, яблоко, персик),
+---
+
+# SentenceTransformer based on ai-forever/ruRoberta-large
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [ai-forever/ruRoberta-large](https://huggingface.co/ai-forever/ruRoberta-large). It maps sentences & paragraphs to a 1024-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:** [ai-forever/ruRoberta-large](https://huggingface.co/ai-forever/ruRoberta-large) <!-- at revision 5192d064ca6ac67c14c40e017ce41612e010f05f -->
+- **Maximum Sequence Length:** 514 tokens
+- **Output Dimensionality:** 1024 tokens
+- **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': 514, 'do_lower_case': False}) with Transformer model: RobertaModel 
+  (1): Pooling({'word_embedding_dimension': 1024, '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("sentence_transformers_model_id")
+# Run inference
+sentences = [
+    'НЕТ ДО 20.04!!!!!!!! 12.01.16  Аллергокомпонент f77 - бета-лактоглобулин nBos d 5, IgE (ImmunoCAP)',
+    'Панель аллергенов животных № 70 IgE (эпителий морской свинки, эпителий кролика, хомяк, крыса, мышь),',
+    'Ультразвуковое исследование плода',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 1024]
+
+# 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.*
+-->
+
+<!--
+## 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: 19,383 training samples
+* Columns: <code>sentence_0</code> and <code>sentence_1</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence_0                                                                        | sentence_1                                                                         |
+  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                             |
+  | details | <ul><li>min: 5 tokens</li><li>mean: 30.0 tokens</li><li>max: 121 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 30.73 tokens</li><li>max: 105 tokens</li></ul> |
+* Samples:
+  | sentence_0                                                                                       | sentence_1                                                                                    |
+  |:-------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------|
+  | <code>Ингибитор VIII фактора</code>                                                              | <code>Исследование уровня антигена фактора Виллебранда</code>                                 |
+  | <code>13.01.02 Антитела к экстрагируемому нуклеарному АГ (ЭНА/ENA-скрин), сыворотка крови</code> | <code>Антитела к экстрагируемому ядерному антигену, кач.</code>                               |
+  | <code>Нет 12.4.092 Аллерген f203 - фисташковые орехи, IgE</code>                                 | <code>Панель аллергенов деревьев № 2 IgE (клен ясенелистный, тополь, вяз, дуб, пекан),</code> |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `per_device_train_batch_size`: 4
+- `per_device_eval_batch_size`: 4
+- `num_train_epochs`: 11
+- `multi_dataset_batch_sampler`: round_robin
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: no
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 4
+- `per_device_eval_batch_size`: 4
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_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
+- `num_train_epochs`: 11
+- `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`: 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}
+- `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
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: round_robin
+
+</details>
+
+### Training Logs
+<details><summary>Click to expand</summary>
+
+| Epoch   | Step  | Training Loss |
+|:-------:|:-----:|:-------------:|
+| 0.1032  | 500   | 0.7937        |
+| 0.2064  | 1000  | 0.5179        |
+| 0.3095  | 1500  | 0.5271        |
+| 0.4127  | 2000  | 0.5696        |
+| 0.5159  | 2500  | 0.5232        |
+| 0.6191  | 3000  | 0.6401        |
+| 0.7222  | 3500  | 0.6337        |
+| 0.8254  | 4000  | 0.9436        |
+| 0.9286  | 4500  | 1.3872        |
+| 1.0318  | 5000  | 1.3834        |
+| 1.1350  | 5500  | 0.9831        |
+| 1.2381  | 6000  | 1.0122        |
+| 1.3413  | 6500  | 1.3708        |
+| 1.4445  | 7000  | 1.3794        |
+| 1.5477  | 7500  | 1.3784        |
+| 1.6508  | 8000  | 1.3856        |
+| 1.7540  | 8500  | 1.3809        |
+| 1.8572  | 9000  | 1.3776        |
+| 1.9604  | 9500  | 1.0041        |
+| 2.0636  | 10000 | 0.8559        |
+| 2.1667  | 10500 | 0.8531        |
+| 2.2699  | 11000 | 0.8446        |
+| 2.3731  | 11500 | 0.8487        |
+| 2.4763  | 12000 | 1.0807        |
+| 2.5794  | 12500 | 1.3792        |
+| 2.6826  | 13000 | 1.3923        |
+| 2.7858  | 13500 | 1.3787        |
+| 2.8890  | 14000 | 1.3803        |
+| 2.9922  | 14500 | 1.3641        |
+| 3.0953  | 15000 | 1.3725        |
+| 3.1985  | 15500 | 1.3624        |
+| 3.3017  | 16000 | 1.3659        |
+| 3.4049  | 16500 | 1.3609        |
+| 3.5080  | 17000 | 1.3496        |
+| 3.6112  | 17500 | 1.3639        |
+| 3.7144  | 18000 | 1.3487        |
+| 3.8176  | 18500 | 1.3463        |
+| 3.9208  | 19000 | 1.336         |
+| 4.0239  | 19500 | 1.3451        |
+| 4.1271  | 20000 | 1.3363        |
+| 4.2303  | 20500 | 1.3411        |
+| 4.3335  | 21000 | 1.3376        |
+| 4.4366  | 21500 | 1.3294        |
+| 4.5398  | 22000 | 1.3281        |
+| 4.6430  | 22500 | 1.3323        |
+| 4.7462  | 23000 | 1.3411        |
+| 4.8494  | 23500 | 1.3162        |
+| 4.9525  | 24000 | 1.3204        |
+| 5.0557  | 24500 | 1.324         |
+| 5.1589  | 25000 | 1.3253        |
+| 5.2621  | 25500 | 1.3283        |
+| 5.3652  | 26000 | 1.3298        |
+| 5.4684  | 26500 | 1.3144        |
+| 5.5716  | 27000 | 1.3162        |
+| 5.6748  | 27500 | 1.3148        |
+| 5.7780  | 28000 | 1.3254        |
+| 5.8811  | 28500 | 1.319         |
+| 5.9843  | 29000 | 1.3134        |
+| 6.0875  | 29500 | 1.3184        |
+| 6.1907  | 30000 | 1.3049        |
+| 6.2939  | 30500 | 1.3167        |
+| 6.3970  | 31000 | 1.3192        |
+| 6.5002  | 31500 | 1.2926        |
+| 6.6034  | 32000 | 1.3035        |
+| 6.7066  | 32500 | 1.3117        |
+| 6.8097  | 33000 | 1.3093        |
+| 6.9129  | 33500 | 1.278         |
+| 7.0161  | 34000 | 1.3143        |
+| 7.1193  | 34500 | 1.3144        |
+| 7.2225  | 35000 | 1.304         |
+| 7.3256  | 35500 | 1.3066        |
+| 7.4288  | 36000 | 1.2916        |
+| 7.5320  | 36500 | 1.2943        |
+| 7.6352  | 37000 | 1.2883        |
+| 7.7383  | 37500 | 1.3014        |
+| 7.8415  | 38000 | 1.3005        |
+| 7.9447  | 38500 | 1.2699        |
+| 8.0479  | 39000 | 1.3042        |
+| 8.1511  | 39500 | 1.289         |
+| 8.2542  | 40000 | 1.3012        |
+| 8.3574  | 40500 | 1.3017        |
+| 8.4606  | 41000 | 1.272         |
+| 8.5638  | 41500 | 1.2939        |
+| 8.6669  | 42000 | 1.2764        |
+| 8.7701  | 42500 | 1.2908        |
+| 8.8733  | 43000 | 1.2619        |
+| 8.9765  | 43500 | 1.2791        |
+| 9.0797  | 44000 | 1.2722        |
+| 9.1828  | 44500 | 1.278         |
+| 9.2860  | 45000 | 1.2911        |
+| 9.3892  | 45500 | 1.2791        |
+| 9.4924  | 46000 | 1.2791        |
+| 9.5955  | 46500 | 1.2782        |
+| 9.6987  | 47000 | 1.2789        |
+| 9.8019  | 47500 | 1.2858        |
+| 9.9051  | 48000 | 1.2601        |
+| 10.0083 | 48500 | 1.29          |
+| 10.1114 | 49000 | 1.276         |
+| 10.2146 | 49500 | 1.2801        |
+| 10.3178 | 50000 | 1.2853        |
+| 10.4210 | 50500 | 1.2655        |
+| 10.5241 | 51000 | 1.271         |
+| 10.6273 | 51500 | 1.2633        |
+| 10.7305 | 52000 | 1.2565        |
+| 10.8337 | 52500 | 1.2755        |
+| 10.9369 | 53000 | 1.2567        |
+
+</details>
+
+### Framework Versions
+- Python: 3.10.12
+- Sentence Transformers: 3.0.1
+- Transformers: 4.41.2
+- PyTorch: 2.3.0+cu121
+- Accelerate: 0.31.0
+- Datasets: 2.20.0
+- 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",
+}
+```
+
+#### MultipleNegativesRankingLoss
+```bibtex
+@misc{henderson2017efficient,
+    title={Efficient Natural Language Response Suggestion for Smart Reply}, 
+    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
+    year={2017},
+    eprint={1705.00652},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

@@ -0,0 +1,28 @@
+{
+  "_name_or_path": "ai-forever/ruRoberta-large",
+  "architectures": [
+    "RobertaModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 1,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "roberta",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.41.2",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 50265
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.0.1",
+    "transformers": "4.41.2",
+    "pytorch": "2.3.0+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

modules.json

@@ -0,0 +1,14 @@
+[
+  {
+    "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

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 514,
+  "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,51 @@
+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": true,
+    "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": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,57 @@
+{
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "4": {
+      "content": "<mask>",
+      "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>",
+  "errors": "replace",
+  "mask_token": "<mask>",
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "tokenizer_class": "RobertaTokenizer",
+  "trim_offsets": true,
+  "unk_token": "<unk>"
+}