Add new SentenceTransformer model.

.gitattributes

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

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": true,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

README.md

@@ -0,0 +1,501 @@
+---
+language: []
+library_name: sentence-transformers
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- dataset_size:100K<n<1M
+- loss:TripletLoss
+base_model: FacebookAI/xlm-roberta-base
+metrics:
+- cosine_accuracy
+- dot_accuracy
+- manhattan_accuracy
+- euclidean_accuracy
+- max_accuracy
+widget:
+- source_sentence: Skip
+  sentences:
+  - Ships
+  - Kapital akcyjny
+  - Other finance income
+- source_sentence: IIII
+  sentences:
+  - iii
+  - Gauti dividendai
+  - Loans given
+- source_sentence: IVE
+  sentences:
+  - HH
+  - Koszty finansowe
+  - Current borrowings
+- source_sentence: K K
+  sentences:
+  - TOTAL ACTIF
+  - Nuomos mokejimai
+  - Accruals
+- source_sentence: Sales
+  sentences:
+  - Revenue
+  - Operating profit
+  - Current borrowings
+pipeline_tag: sentence-similarity
+model-index:
+- name: SentenceTransformer based on FacebookAI/xlm-roberta-base
+  results:
+  - task:
+      type: triplet
+      name: Triplet
+    dataset:
+      name: Unknown
+      type: unknown
+    metrics:
+    - type: cosine_accuracy
+      value: 0.9987885552019722
+      name: Cosine Accuracy
+    - type: dot_accuracy
+      value: 0.001529316610921369
+      name: Dot Accuracy
+    - type: manhattan_accuracy
+      value: 0.9975135360413657
+      name: Manhattan Accuracy
+    - type: euclidean_accuracy
+      value: 0.9990958312877694
+      name: Euclidean Accuracy
+    - type: max_accuracy
+      value: 0.9990958312877694
+      name: Max Accuracy
+---
+
+# SentenceTransformer based on FacebookAI/xlm-roberta-base
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) <!-- at revision e73636d4f797dec63c3081bb6ed5c7b0bb3f2089 -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 768 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': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("slimaneMakh/triplet_CloseHlabel_farLabel_andnegativ-1M-5eps-XLMR_29may")
+# Run inference
+sentences = [
+    'Sales',
+    'Revenue',
+    'Operating profit',
+]
+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
+
+#### Triplet
+
+* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
+
+| Metric             | Value      |
+|:-------------------|:-----------|
+| cosine_accuracy    | 0.9988     |
+| dot_accuracy       | 0.0015     |
+| manhattan_accuracy | 0.9975     |
+| euclidean_accuracy | 0.9991     |
+| **max_accuracy**   | **0.9991** |
+
+<!--
+## 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: 660,643 training samples
+* Columns: <code>anchor_label</code>, <code>pos_hlabel</code>, and <code>neg_hlabel</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor_label                                                                      | pos_hlabel                                                                       | neg_hlabel                                                                       |
+  |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                           | string                                                                           |
+  | details | <ul><li>min: 3 tokens</li><li>mean: 11.86 tokens</li><li>max: 39 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 9.06 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 7.99 tokens</li><li>max: 25 tokens</li></ul> |
+* Samples:
+  | anchor_label                                 | pos_hlabel                                 | neg_hlabel                                                                    |
+  |:---------------------------------------------|:-------------------------------------------|:------------------------------------------------------------------------------|
+  | <code>Basic earnings (loss) per share</code> | <code>Tavakasum kahjum aktsia kohta</code> | <code>II Kapital z nadwyzki wartosci emisyjnej ponad wartosc nominalna</code> |
+  | <code>Comprehensive income</code>            | <code>Suma dochodow calkowitych</code>     | <code>dont Marques</code>                                                     |
+  | <code>Cash and cash equivalents</code>       | <code>Cash and cash equivalents</code>     | <code>Cars incl prepayments</code>                                            |
+* Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters:
+  ```json
+  {
+      "distance_metric": "TripletDistanceMetric.EUCLIDEAN",
+      "triplet_margin": 5
+  }
+  ```
+
+### Evaluation Dataset
+
+#### Unnamed Dataset
+
+
+* Size: 283,133 evaluation samples
+* Columns: <code>anchor_label</code>, <code>pos_hlabel</code>, and <code>neg_hlabel</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor_label                                                                      | pos_hlabel                                                                       | neg_hlabel                                                                       |
+  |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                           | string                                                                           |
+  | details | <ul><li>min: 3 tokens</li><li>mean: 11.78 tokens</li><li>max: 37 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 9.22 tokens</li><li>max: 39 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 8.12 tokens</li><li>max: 29 tokens</li></ul> |
+* Samples:
+  | anchor_label                                                                    | pos_hlabel                                             | neg_hlabel                           |
+  |:--------------------------------------------------------------------------------|:-------------------------------------------------------|:-------------------------------------|
+  | <code>Deferred tax assets</code>                                                | <code>Deferred tax assets</code>                       | <code>Immateriella tillgangar</code> |
+  | <code>Equity</code>                                                             | <code>EGET KAPITAL inklusive periodens resultat</code> | <code>Materials</code>               |
+  | <code>Adjustments for decrease (increase) in other operating receivables</code> | <code>Okning av ovriga rorelsetillgangar</code>        | <code>Rorelseresultat</code>         |
+* Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters:
+  ```json
+  {
+      "distance_metric": "TripletDistanceMetric.EUCLIDEAN",
+      "triplet_margin": 5
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `num_train_epochs`: 1
+- `warmup_ratio`: 0.1
+- `batch_sampler`: no_duplicates
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `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
+- `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`: 1
+- `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
+- `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}
+- `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
+- `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_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch  | Step  | Training Loss | loss   | max_accuracy |
+|:------:|:-----:|:-------------:|:------:|:------------:|
+| 0.0121 | 500   | 3.7705        | -      | -            |
+| 0.0242 | 1000  | 1.4084        | -      | -            |
+| 0.0363 | 1500  | 0.7062        | -      | -            |
+| 0.0484 | 2000  | 0.5236        | -      | -            |
+| 0.0605 | 2500  | 0.4348        | -      | -            |
+| 0.0727 | 3000  | 0.3657        | -      | -            |
+| 0.0848 | 3500  | 0.3657        | -      | -            |
+| 0.0969 | 4000  | 0.2952        | -      | -            |
+| 0.1090 | 4500  | 0.3805        | -      | -            |
+| 0.1211 | 5000  | 0.3255        | -      | -            |
+| 0.1332 | 5500  | 0.2621        | -      | -            |
+| 0.1453 | 6000  | 0.2377        | -      | -            |
+| 0.1574 | 6500  | 0.2139        | -      | -            |
+| 0.1695 | 7000  | 0.2085        | -      | -            |
+| 0.1816 | 7500  | 0.1809        | -      | -            |
+| 0.1937 | 8000  | 0.1711        | -      | -            |
+| 0.2059 | 8500  | 0.1608        | -      | -            |
+| 0.2180 | 9000  | 0.1808        | -      | -            |
+| 0.2301 | 9500  | 0.1553        | -      | -            |
+| 0.2422 | 10000 | 0.1417        | -      | -            |
+| 0.2543 | 10500 | 0.1329        | -      | -            |
+| 0.2664 | 11000 | 0.1689        | -      | -            |
+| 0.2785 | 11500 | 0.1292        | -      | -            |
+| 0.2906 | 12000 | 0.1181        | -      | -            |
+| 0.3027 | 12500 | 0.1223        | -      | -            |
+| 0.3148 | 13000 | 0.129         | -      | -            |
+| 0.3269 | 13500 | 0.0911        | -      | -            |
+| 0.3391 | 14000 | 0.113         | -      | -            |
+| 0.3512 | 14500 | 0.0955        | -      | -            |
+| 0.3633 | 15000 | 0.108         | -      | -            |
+| 0.3754 | 15500 | 0.094         | -      | -            |
+| 0.3875 | 16000 | 0.0947        | -      | -            |
+| 0.3996 | 16500 | 0.0748        | -      | -            |
+| 0.4117 | 17000 | 0.0699        | -      | -            |
+| 0.4238 | 17500 | 0.0707        | -      | -            |
+| 0.4359 | 18000 | 0.0768        | -      | -            |
+| 0.4480 | 18500 | 0.0805        | -      | -            |
+| 0.4601 | 19000 | 0.0705        | -      | -            |
+| 0.4723 | 19500 | 0.069         | -      | -            |
+| 0.4844 | 20000 | 0.072         | -      | -            |
+| 0.4965 | 20500 | 0.0669        | -      | -            |
+| 0.5086 | 21000 | 0.066         | -      | -            |
+| 0.5207 | 21500 | 0.0624        | -      | -            |
+| 0.5328 | 22000 | 0.0687        | -      | -            |
+| 0.5449 | 22500 | 0.076         | -      | -            |
+| 0.5570 | 23000 | 0.0563        | -      | -            |
+| 0.5691 | 23500 | 0.0594        | -      | -            |
+| 0.5812 | 24000 | 0.0524        | -      | -            |
+| 0.5933 | 24500 | 0.0528        | -      | -            |
+| 0.6055 | 25000 | 0.0448        | -      | -            |
+| 0.6176 | 25500 | 0.041         | -      | -            |
+| 0.6297 | 26000 | 0.0397        | -      | -            |
+| 0.6418 | 26500 | 0.0489        | -      | -            |
+| 0.6539 | 27000 | 0.0595        | -      | -            |
+| 0.6660 | 27500 | 0.034         | -      | -            |
+| 0.6781 | 28000 | 0.0569        | -      | -            |
+| 0.6902 | 28500 | 0.0467        | -      | -            |
+| 0.7023 | 29000 | 0.0323        | -      | -            |
+| 0.7144 | 29500 | 0.0428        | -      | -            |
+| 0.7266 | 30000 | 0.0344        | -      | -            |
+| 0.7387 | 30500 | 0.029         | -      | -            |
+| 0.7508 | 31000 | 0.0418        | -      | -            |
+| 0.7629 | 31500 | 0.0285        | -      | -            |
+| 0.7750 | 32000 | 0.0425        | -      | -            |
+| 0.7871 | 32500 | 0.0266        | -      | -            |
+| 0.7992 | 33000 | 0.0325        | -      | -            |
+| 0.8113 | 33500 | 0.0215        | -      | -            |
+| 0.8234 | 34000 | 0.0316        | -      | -            |
+| 0.8355 | 34500 | 0.0286        | -      | -            |
+| 0.8476 | 35000 | 0.0285        | -      | -            |
+| 0.8598 | 35500 | 0.0284        | -      | -            |
+| 0.8719 | 36000 | 0.0147        | -      | -            |
+| 0.8840 | 36500 | 0.0217        | -      | -            |
+| 0.8961 | 37000 | 0.0311        | -      | -            |
+| 0.9082 | 37500 | 0.0202        | -      | -            |
+| 0.9203 | 38000 | 0.0236        | -      | -            |
+| 0.9324 | 38500 | 0.0201        | -      | -            |
+| 0.9445 | 39000 | 0.0246        | -      | -            |
+| 0.9566 | 39500 | 0.0177        | -      | -            |
+| 0.9687 | 40000 | 0.0173        | -      | -            |
+| 0.9808 | 40500 | 0.0202        | -      | -            |
+| 0.9930 | 41000 | 0.017         | -      | -            |
+| 1.0    | 41291 | -             | 0.0140 | 0.9991       |
+
+
+### Framework Versions
+- Python: 3.10.13
+- Sentence Transformers: 3.0.0
+- Transformers: 4.39.3
+- PyTorch: 2.1.2
+- Accelerate: 0.28.0
+- Datasets: 2.18.0
+- Tokenizers: 0.15.2
+
+## 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",
+}
+```
+
+#### TripletLoss
+```bibtex
+@misc{hermans2017defense,
+    title={In Defense of the Triplet Loss for Person Re-Identification}, 
+    author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
+    year={2017},
+    eprint={1703.07737},
+    archivePrefix={arXiv},
+    primaryClass={cs.CV}
+}
+```
+
+<!--
+## 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": "xlm-roberta-base",
+  "architectures": [
+    "XLMRobertaModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "xlm-roberta",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "output_past": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.39.3",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.0.0",
+    "transformers": "4.39.3",
+    "pytorch": "2.1.2"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cb1f620ceabb3b9cf7aa761b0d2090b034e28bf8030cbd05f86fd373cb6596f8
+size 1112197096

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

sentencepiece.bpe.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cfc8146abe2a0488e9e2a0c56de7952f7c11ab059eca145a0a727afce0db2865
+size 5069051

special_tokens_map.json

@@ -0,0 +1,15 @@
+{
+  "bos_token": "<s>",
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "unk_token": "<unk>"
+}

tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f1cc44ad7faaeec47241864835473fd5403f2da94673f3f764a77ebcb0a803ec
+size 17083009

tokenizer_config.json

@@ -0,0 +1,54 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "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
+    },
+    "250001": {
+      "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>",
+  "mask_token": "<mask>",
+  "model_max_length": 512,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "tokenizer_class": "XLMRobertaTokenizer",
+  "unk_token": "<unk>"
+}