Add new SentenceTransformer model

.gitattributes

@@ -33,3 +33,5 @@ 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
+unigram.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 +1,435 @@
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:23525
+- loss:CosineSimilarityLoss
+base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
+widget:
+- source_sentence: Монголбанк, Сангийн яам болон Европын сэргээн босголт, хөгжлийн
+    банкны санамж бичиг
+  sentences:
+  - '"Жүжгүүд нь хүүхдийн урлаг гоо зүйн боловсролыг дээшлүүлэхэд хувь нэмэр оруулна."'
+  - “Үзэхийн хязгаар” ном хоёр дэлгүүрт борлуулалттай байв
+  - Зээлийн эрсдэлийг хуваан үүрэлцэх гэрээ.
+- source_sentence: Улсын дуурь, бүжгийн эрдмийн театрт номын нээлт болно.
+  sentences:
+  - УДБЭТ-д номын нээлт болно.
+  - Хүндрэлээс гарах арга хэмжээ авахгүй бол эдийн засгийн өсөлт 2 хувиас доошилж,
+    ажилгүйдлийн төвшин ч 10 хувиас дээшилж, экспортын хэмжээ таван тэрбумам.доллараас
+    доошлох магадлалтай аж.
+  - Дахин давтан хэлчхэд урлаг бол үзүүлдэг, шинжлэх ухаан нотолдог гэдэг.
+- source_sentence: ОХУ, БНХАУ-ыг Монгол Улсын нутаг дэвсгэрээр холбоно
+  sentences:
+  - Нийслэлийн 24 дүгээр сургуулийг төгссөн.
+  - Зураг нь гайхамшигтай гэж Кейт үнэлжээ.
+  - Гурван улс дамнасан худалдаа эргэлтийг дамжин өнгөрүүлнэ
+- source_sentence: “Драмын жүжгийн төрөл”, “Хүүхдийн жүжгийн төрөл”, “Дуулалт жүжгийн
+    төрөл”, “Нэг хүний жүжгийн төрөл”-үүдэд 40 гаруй жүжиг санал болгосон
+  sentences:
+  - Дуурь, бүжгийн эрдмийн театрын уран бүтээлчид буюу балет анги, хөгжим анги, найрал
+    дуу, гоцлол дуучид тайзнаа Итали, Орос, Францын сор болсон бүтээлийг түүвэрлэн
+    хүргэж, үзэгчдийг зуун дамнуулан цаг хугацаагаар аялуулан сонгодог тансаг орчинд
+    тайз дэлгэцээрээ дамжуулан урьсан гээд энэ үдшийн онцлог олон байлаа
+  - 20 уран бүтээл, 80 гаруй уран бүтээлч чансаагаа сорьж байгаа ажээ.
+  - Цомогт шинэ уран бүтээлүүд багтсан.
+- source_sentence: Олон улсын наадмын шалгаруулалт
+  sentences:
+  - Мянган тонн үр олгогдсон.
+  - Мөн нийт экспортын хэмжээ 10 хувиар, түүн дунд нүүрсний экспорт 50 хувиар  буурсан
+    юм.
+  - Драмын урлагийн шилдгүүдийг тодруулдаг наадам.
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+metrics:
+- pearson_cosine
+- spearman_cosine
+model-index:
+- name: SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
+  results:
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: dev t
+      type: dev-t
+    metrics:
+    - type: pearson_cosine
+      value: 0.573972184548268
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.5460401569671698
+      name: Spearman Cosine
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: test t
+      type: test-t
+    metrics:
+    - type: pearson_cosine
+      value: 0.5937906658169482
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.5612769176839287
+      name: Spearman Cosine
+---
+
+# SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) on the csv dataset. 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:** [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) <!-- at revision 8d6b950845285729817bf8e1af1861502c2fed0c -->
+- **Maximum Sequence Length:** 128 tokens
+- **Output Dimensionality:** 384 dimensions
+- **Similarity Function:** Cosine Similarity
+- **Training Dataset:**
+    - csv
+<!-- - **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': 128, 'do_lower_case': False}) with Transformer model: 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})
+)
+```
+
+## 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("gmunkhtur/paraphrase-mongolian-minilm-mntoken")
+# Run inference
+sentences = [
+    'Олон улсын наадмын шалгаруулалт',
+    'Драмын урлагийн шилдгүүдийг тодруулдаг наадам.',
+    'Мөн нийт экспортын хэмжээ 10 хувиар, түүн дунд нүүрсний экспорт 50 хувиар\xa0 буурсан юм.',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 384]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+## Evaluation
+
+### Metrics
+
+#### Semantic Similarity
+
+* Datasets: `dev-t` and `test-t`
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric              | dev-t     | test-t     |
+|:--------------------|:----------|:-----------|
+| pearson_cosine      | 0.574     | 0.5938     |
+| **spearman_cosine** | **0.546** | **0.5613** |
+
+<!--
+## 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
+
+#### csv
+
+* Dataset: csv
+* Size: 23,525 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: 3 tokens</li><li>mean: 12.82 tokens</li><li>max: 90 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 12.44 tokens</li><li>max: 77 tokens</li></ul> | <ul><li>min: 0.02</li><li>mean: 0.49</li><li>max: 1.0</li></ul> |
+* Samples:
+  | sentence1                                                                                                                                                                               | sentence2                                                                         | score                           |
+  |:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:--------------------------------|
+  | <code>Хүн амын нягтаршил багатай, газар хөдлөлийн идэвхигүй бүс, газрын гадарга нь тэгш, үер усны давтамж бага газарт Цөмийн энергийн станцийг барьж байгуулах шаардлагатай гэнэ</code> | <code>Энэ станцад захын нэг дээд сургууль эзэмшсэн нөхөр очоод ажиллахгүй.</code> | <code>0.2018195390701294</code> |
+  | <code>Уг компани тендерт гадаадынхныг урьсан ба өрөгдлийг нь зургадугаар сарын 3 хүртэл хүлээн авсан байна</code>                                                                       | <code>«Коммерсантъ» сонин 24-ний өдрийн дугаартаа өгүүлсэн байна</code>           | <code>0.2372543811798095</code> |
+  | <code>Би “Өүлэн эх”-ийг анх бүтээсэн</code>                                                                                                                                             | <code>Би “Хорин нэгэн зул”-ыг анх бүтээсэн.</code>                                | <code>0.6730476021766663</code> |
+* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
+  ```json
+  {
+      "loss_fct": "torch.nn.modules.loss.MSELoss"
+  }
+  ```
+
+### Evaluation Dataset
+
+#### csv
+
+* Dataset: csv
+* Size: 23,525 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: 4 tokens</li><li>mean: 12.78 tokens</li><li>max: 123 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 12.69 tokens</li><li>max: 59 tokens</li></ul> | <ul><li>min: -0.04</li><li>mean: 0.48</li><li>max: 0.98</li></ul> |
+* Samples:
+  | sentence1                                                                                       | sentence2                                                                                                                                                            | score                           |
+  |:------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------|
+  | <code>Анхны тоглолт маань одоо бодоход үнэхээр гоё болж байсан</code>                           | <code>Яг ямар чиглэлээр тоглохоо мэдэхгүй жаахан охин байсан ч би маш их зүйл сурсан</code>                                                                          | <code>0.2749532461166382</code> |
+  | <code>"Домогт Ану хатан" нь Монголын түүхэн дэх хатан хааны тухай өгүүлдэг</code>               | <code>"Домогт Ану хатан" нь Б.Шүүдэрцэцэгийн бүтээл юм.</code>                                                                                                       | <code>0.3653741478919983</code> |
+  | <code>Советийн хурлаар "Эрдэнэт" болон "Монголросцветмет нэгдэл"-ийн талаар ярилцах ажээ</code> | <code>Асгатын мөнгөний ордыг түшиглэн Орос-Монголын хамтарсан компани байгуулахаар болсон бөгөөд энэ асуудлыг хуралдаанаар хөндөнө гэдгийг эх сурвалж хэлсэн.</code> | <code>0.599888801574707</code>  |
+* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
+  ```json
+  {
+      "loss_fct": "torch.nn.modules.loss.MSELoss"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `num_train_epochs`: 5
+- `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`: 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`: 5
+- `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}
+- `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
+- `dispatch_batches`: None
+- `split_batches`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `use_liger_kernel`: False
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: False
+- `prompts`: None
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss | Validation Loss | dev-t_spearman_cosine | test-t_spearman_cosine |
+|:------:|:----:|:-------------:|:---------------:|:---------------------:|:----------------------:|
+| 0      | 0    | -             | -               | 0.2295                | -                      |
+| 0.5663 | 500  | 0.0403        | -               | -                     | -                      |
+| 1.1325 | 1000 | 0.0332        | 0.0320          | 0.5316                | -                      |
+| 1.6988 | 1500 | 0.0188        | -               | -                     | -                      |
+| 2.2650 | 2000 | 0.0135        | 0.0311          | 0.5361                | -                      |
+| 2.8313 | 2500 | 0.0085        | -               | -                     | -                      |
+| 3.3975 | 3000 | 0.0074        | 0.0310          | 0.5352                | -                      |
+| 3.9638 | 3500 | 0.0054        | -               | -                     | -                      |
+| 4.5300 | 4000 | 0.0045        | 0.0308          | 0.5460                | -                      |
+| 5.0    | 4415 | -             | -               | -                     | 0.5613                 |
+
+
+### Framework Versions
+- Python: 3.10.12
+- Sentence Transformers: 3.3.1
+- Transformers: 4.47.1
+- PyTorch: 2.5.1+cu121
+- Accelerate: 1.2.1
+- Datasets: 3.2.0
+- 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",
+}
+```
+
+<!--
+## 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 +1,26 @@
+{
+  "_name_or_path": "sentence-transformers/paraphrase-multilingual-minilm-l12-v2",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "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",
+  "torch_dtype": "float32",
+  "transformers_version": "4.47.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 250037
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.3.1",
+    "transformers": "4.47.1",
+    "pytorch": "2.5.1+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e637fb06c96353e0b14cb52592ce66a015f38d05eb21bbbd77c0f9bc2a8830a1
+size 470637416

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": 128,
+  "do_lower_case": false
+}

special_tokens_map.json

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

tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4275a06916f5096567b8124c5a1824e7b247accaa84a128c932bce68b344c182
+size 18342693

tokenizer_config.json

@@ -0,0 +1,65 @@
+{
+  "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
+    },
+    "4": {
+      "content": "<mask>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "<s>",
+  "do_lower_case": true,
+  "eos_token": "</s>",
+  "extra_special_tokens": {},
+  "mask_token": "<mask>",
+  "max_length": 128,
+  "model_max_length": 512,
+  "pad_to_multiple_of": null,
+  "pad_token": "<pad>",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "</s>",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "<unk>"
+}

unigram.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:185af83f47e4ac151b4773be89bd07943c9263663b3ad514bb5a4f86dd1e8868
+size 16022971