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sc40/1_Pooling/config.json

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+{
+    "embedding_dimension": 768,
+    "pooling_mode": "mean",
+    "include_prompt": true
+}

sc40/README.md

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+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:715
+- loss:MultipleNegativesRankingLoss
+widget:
+- source_sentence: Amafaranga amaze imyaka 5 kuri konti zitagikoreshwa muri banki
+    z’ubucuruzi yoherezwa muri Banki nkuru y’igihugu.
+  sentences:
+  - Ntabwo ari umukobwa wese ushobora kwihanganira umusore umucunga cyane ndetse akamuha
+    amategeko akabije, hari ubona atazabishobora agahitamo kwigendera kandi aribwo
+    umubano wanyu wari ugitangira.
+  - Comfort People Ministries yahuje imbaraga na Sintex mu ndirimbo nshya ‘Jambo ryawe’
+  - Iyo ayo mafaranga amaze imyaka 5 muri Banki nkuru y’igihugu ba nyirayo bataje
+    kuyakurikirana, ashyirwa mu mjutungo wa leta.
+- source_sentence: Juru Ornella w’imyaka 20 y’amavuko ku wa 2 Ukwakira 2020, nibwo
+    yashyize hanze indirimbo yise ‘Pango’ itunganyijwe mu buryo bw’amajwi n’amashusho.
+  sentences:
+  - Abakinnyi ba Paris Saint-Germain barimo Kylian Mbappé bifatanyije n’u Rwanda mu
+    Kwibuka ku nshuro ya 27 Jenoside yakorewe Abatutsi
+  - Kuri ubu uyu muhanzi yashyize hanze iyitwa ‘Sound’ itunganyijwe mu buryo bw’amajwi
+    avuga ko amashusho yayo ari hafi.
+  - Muri rusange inyigo zagaragaje ko nta rugero runaka rw’ubushake umuntu muzima
+    yakabaye afite, ahubwo buri wese agira urugero rwe rushobora kwiyongera cyangwa
+    rukagabanyuka bitewe n’impamvu.
+- source_sentence: Abatanga serivisi za Mobile Money, Tigo Cash na Airtel Money bagomba
+    gukora ibishoboka byose ntihagire uwundi uwumenya; kandi uwibwe akwiriye kubimenyesha
+    Polisi mu maguru mashya."
+  sentences:
+  - Ndayishimiye yavuze ko ubutumwa buri muri iyi ndirimbo bugamije kubwira abantu
+    ko bafite ubutunzi bukomeye bwo kumenya Imana.
+  - Adrien ubusanzwe atuye muri Leta Zunze Ubumwe za Amerika mu mujyi wa Texas.
+  - Gaga yakomeje avuga ko batangije uburyo bwo guhuza serivisi za Mobile Money, Tigo
+    Cash na Airtel Money n'amabanki hakoreshejwe umuyoboro bise eHuriro.
+- source_sentence: Algeria niyo yafunguye amazamu ku munota wa 10 ku gitego cyatsinzwe
+    na Farouk Chafai.
+  sentences:
+  - Umuyobozi wa Yego Innovision, Karanvir Singh, yavuze ko yishimiye gukorana na
+    Spenn kuko bizajya byorohereza abashoferi babo kubona amafaranga yabo yose kandi
+    mu gihe gito.
+  - Hari nyuma y’uko iyi kipe yari imaze guhagarika imishahara y’abakinnyi kubera
+    icyorezo cya Covid-19.
+  - Ku munota wa 29, Algeria yatsinze icya 3 cyatsinzwe nanone na Farouk Chafai.
+- source_sentence: Minisitiri Biruta, yavuze ko gucana Urumuri Rutazima bigaragaza
+    kwibuka Jenoside yakorewe Abatutsi, kugarurira agaciro abazize Jenoside ndetse
+    n'u Rwanda muri rusange.
+  sentences:
+  - Mu karere ka Burera, Urumuri Rutazima rwakiriwe ku Rwibutso rw'abazize Jenoside
+    yakorewe Abatutsi, ruri mu murenge wa Rugarama.
+  - Abakinnyi 21 b’u Rwanda bari muri Mexique bose bavutse nyuma ya tariki ya 7 Mata
+    1994, ubwo Jenoside yakorerwaga Abatutsi yatangiraga, nyamara babaye Abambasaderi
+    b’igihugu.
+  - Kuva ubwo rero babona ingaruka mbi ku kintu cyagizwe ibanze bitari mbonera, bati
+    “ibijya gucika inkungu ibijya imbere”.
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+metrics:
+- pearson_cosine
+- spearman_cosine
+model-index:
+- name: SentenceTransformer
+  results:
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: semrel2024 dev
+      type: semrel2024_dev
+    metrics:
+    - type: pearson_cosine
+      value: 0.5863880798537018
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.596229941564541
+      name: Spearman Cosine
+---
+
+# SentenceTransformer
+
+This is a [sentence-transformers](https://www.SBERT.net) model trained. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for retrieval.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+<!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
+- **Maximum Sequence Length:** 128 tokens
+- **Output Dimensionality:** 768 dimensions
+- **Similarity Function:** Cosine Similarity
+- **Supported Modality:** Text
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'BertModel'})
+  (1): Pooling({'embedding_dimension': 768, 'pooling_mode': 'mean', '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 = [
+    "Minisitiri Biruta, yavuze ko gucana Urumuri Rutazima bigaragaza kwibuka Jenoside yakorewe Abatutsi, kugarurira agaciro abazize Jenoside ndetse n'u Rwanda muri rusange.",
+    "Mu karere ka Burera, Urumuri Rutazima rwakiriwe ku Rwibutso rw'abazize Jenoside yakorewe Abatutsi, ruri mu murenge wa Rugarama.",
+    'Kuva ubwo rero babona ingaruka mbi ku kintu cyagizwe ibanze bitari mbonera, bati “ibijya gucika inkungu ibijya imbere”.',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 768]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities)
+# tensor([[1.0000, 0.5727, 0.3690],
+#         [0.5727, 1.0000, 0.2020],
+#         [0.3690, 0.2020, 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
+
+* Dataset: `semrel2024_dev`
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.sentence_transformer.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| pearson_cosine      | 0.5864     |
+| **spearman_cosine** | **0.5962** |
+
+<!--
+## 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: 715 training samples
+* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
+* Approximate statistics based on the first 100 samples:
+  |          | anchor                                                                            | positive                                                                          | negative                                                                          |
+  |:---------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
+  | type     | string                                                                            | string                                                                            | string                                                                            |
+  | modality | text                                                                              | text                                                                              | text                                                                              |
+  | details  | <ul><li>min: 6 tokens</li><li>mean: 19.87 tokens</li><li>max: 60 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 14.34 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 14.35 tokens</li><li>max: 30 tokens</li></ul> |
+* Samples:
+  | anchor                                                                                                                                                                                                                                               | positive                                                                                            | negative                                                                                                                                              |
+  |:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>Ubuyobozi bw'Igihugu bw'Inyigisho z'Inyigisho z'Inyigisho z'Inyigisho z'Inyigisho z'Inyigisho z'Inyigisho z'Inyigisho z'Inyigisho z'Inyigisho z'Inyigisho z'Inyigisho z'Inyigisho z'Inyigisho z'Inyigisho z'Inyigisho z'Inyigisho z'Iny</code> | <code>Ubuyobozi bw'Igihugu bwatanze amakuru y'ingirakamaro mu mpinduka z'ubuyobozi bwa Leta.</code> | <code>Gufata imyanzuro irebana n'ibyo Leta yagezeho nta makuru y'ingirakamaro itanga ku birebana no kuvugurura imyanzuro y'ubuyobozi bwa Leta.</code> |
+  | <code>Ca'daan yahise atera hejuru.</code>                                                                                                                                                                                                            | <code>Ca'daan ni we wavuze ko bazaza.</code>                                                        | <code>Ca'daan yarasemereye cyane ati "bose barapfuye"!</code>                                                                                         |
+  | <code>Nubwo hari ibintu byinshi bihinduka, imikorere ikoreshwa muri iki gihe yemera ibyiciro bine by'ibanze by'ubwoko bw'amasezerano ashyirwa mu bikorwa mu gihe cy'ubucuruzi butandukanye.</code>                                                   | <code>Hari amoko ane y'ibanze yemewe n'imikorere iriho muri iki gihe.</code>                        | <code>Nta tandukaniro ry'imikorere y'ubu yemewe.</code>                                                                                               |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 40,
+      "similarity_fct": "cos_sim",
+      "gather_across_devices": false,
+      "directions": [
+          "query_to_doc"
+      ],
+      "partition_mode": "joint",
+      "hardness_mode": null,
+      "hardness_strength": 0.0
+  }
+  ```
+
+### Evaluation Dataset
+
+#### Unnamed Dataset
+
+* Size: 102 evaluation samples
+* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
+* Approximate statistics based on the first 100 samples:
+  |          | sentence1                                                                          | sentence2                                                                         | label                                                         |
+  |:---------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------|
+  | type     | string                                                                             | string                                                                            | float                                                         |
+  | modality | text                                                                               | text                                                                              |                                                               |
+  | details  | <ul><li>min: 13 tokens</li><li>mean: 29.75 tokens</li><li>max: 51 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 28.6 tokens</li><li>max: 42 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.5</li><li>max: 1.0</li></ul> |
+* Samples:
+  | sentence1                                                                                                                                                                                               | sentence2                                                                                                                                                                                  | label             |
+  |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------|
+  | <code>Impamvu zo kwandura indwara zo mu gitsina : Ukumagara mu gitsina kandi bishobora guterwa n'indwara zifata mu gitsina.</code>                                                                      | <code>Impamvu z'ibiyobyabwenge : Itabi, alukolo nyinshi (alcool) bishobora na byo gutera ukumagara mu gitsina biherekejwe no kunanirwa k'ubwonko, no kugira umunaniro umubiri wose.</code> | <code>0.19</code> |
+  | <code>Hakiri kare ku munota wa 18’, Nsengiyumva Moustapha yaboneye Rayon Sports igitego cya mbere mbere yo kungamo icya kabiri ku munota wa 34’ w’umukino.</code>                                       | <code>Nsengiyumva Moustapha yasimbuwe na Sibomana Abouba ku munota wa 65’ mbere naho Nova Bayama asimbura Muhire Kevin ku munota wa 68’ w’umukino.</code>                                  | <code>0.5</code>  |
+  | <code>Iki gikorwa cyo kwibuka ku nshuro ya 19 Jenoside yakorewe abatutsi, cyateguwe n'umuryango Never Again, ugizwe n'abarokotse Jenoside yakorewe abatutsi mu Rwanda ufatanyije n'inshuti zawo.</code> | <code>Mubikorwa biteganyijwe gukorwa n'iri huriro harimo kwifatanya n'Abanyarwanda n'amahanga kwibuka ku nshuro ya 22 Jenoside yakorewe Abatutsi mu 1994.</code>                           | <code>0.56</code> |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 40,
+      "similarity_fct": "cos_sim",
+      "gather_across_devices": false,
+      "directions": [
+          "query_to_doc"
+      ],
+      "partition_mode": "joint",
+      "hardness_mode": null,
+      "hardness_strength": 0.0
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `per_device_train_batch_size`: 64
+- `num_train_epochs`: 1
+- `learning_rate`: 2e-05
+- `warmup_steps`: 0.1
+- `load_best_model_at_end`: True
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `per_device_train_batch_size`: 64
+- `num_train_epochs`: 1
+- `max_steps`: -1
+- `learning_rate`: 2e-05
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: None
+- `warmup_steps`: 0.1
+- `optim`: adamw_torch
+- `optim_args`: None
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `optim_target_modules`: None
+- `gradient_accumulation_steps`: 1
+- `average_tokens_across_devices`: True
+- `max_grad_norm`: 1.0
+- `label_smoothing_factor`: 0.0
+- `bf16`: False
+- `fp16`: False
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `use_liger_kernel`: False
+- `liger_kernel_config`: None
+- `use_cache`: False
+- `neftune_noise_alpha`: None
+- `torch_empty_cache_steps`: None
+- `auto_find_batch_size`: False
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `include_num_input_tokens_seen`: no
+- `log_level`: passive
+- `log_level_replica`: warning
+- `disable_tqdm`: False
+- `project`: huggingface
+- `trackio_space_id`: None
+- `trackio_bucket_id`: None
+- `trackio_static_space_id`: None
+- `per_device_eval_batch_size`: 8
+- `prediction_loss_only`: True
+- `eval_on_start`: False
+- `eval_do_concat_batches`: True
+- `eval_use_gather_object`: False
+- `eval_accumulation_steps`: None
+- `include_for_metrics`: []
+- `batch_eval_metrics`: False
+- `save_only_model`: False
+- `save_on_each_node`: False
+- `enable_jit_checkpoint`: False
+- `push_to_hub`: False
+- `hub_private_repo`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_always_push`: False
+- `hub_revision`: None
+- `load_best_model_at_end`: True
+- `ignore_data_skip`: False
+- `restore_callback_states_from_checkpoint`: False
+- `full_determinism`: False
+- `seed`: 42
+- `data_seed`: None
+- `use_cpu`: False
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `parallelism_config`: None
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `dataloader_prefetch_factor`: None
+- `remove_unused_columns`: True
+- `label_names`: None
+- `train_sampling_strategy`: random
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `ddp_static_graph`: None
+- `ddp_backend`: None
+- `ddp_timeout`: 1800
+- `fsdp`: []
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `deepspeed`: None
+- `debug`: []
+- `skip_memory_metrics`: True
+- `do_predict`: False
+- `resume_from_checkpoint`: None
+- `warmup_ratio`: None
+- `local_rank`: -1
+- `prompts`: None
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+- `router_mapping`: {}
+- `learning_rate_mapping`: {}
+
+</details>
+
+### Training Logs
+| Epoch   | Step   | Validation Loss | semrel2024_dev_spearman_cosine |
+|:-------:|:------:|:---------------:|:------------------------------:|
+| **1.0** | **12** | **0.672**       | **0.5962**                     |
+
+* The bold row denotes the saved checkpoint.
+
+### Training Time
+- **Training**: 24.5 seconds
+- **Evaluation**: 0.6 seconds
+- **Total**: 25.2 seconds
+
+### Framework Versions
+- Python: 3.10.20
+- Sentence Transformers: 5.5.1
+- Transformers: 5.7.0
+- PyTorch: 2.7.1+cu118
+- Accelerate: 1.13.0
+- Datasets: 5.0.0
+- Tokenizers: 0.22.2
+
+## 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",
+}
+```
+
+#### MultipleNegativesRankingLoss
+```bibtex
+@misc{oord2019representationlearningcontrastivepredictive,
+      title={Representation Learning with Contrastive Predictive Coding},
+      author={Aaron van den Oord and Yazhe Li and Oriol Vinyals},
+      year={2019},
+      eprint={1807.03748},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/1807.03748},
+}
+```
+
+<!--
+## 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.*
+-->

sc40/config.json

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+{
+  "add_cross_attention": false,
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": null,
+  "classifier_dropout": null,
+  "dtype": "float32",
+  "eos_token_id": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "is_decoder": false,
+  "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",
+  "tie_word_embeddings": true,
+  "transformers_version": "5.7.0",
+  "type_vocab_size": 2,
+  "use_cache": false,
+  "vocab_size": 30000
+}

sc40/config_sentence_transformers.json

@@ -0,0 +1,14 @@
+{
+  "__version__": {
+    "pytorch": "2.7.1+cu118",
+    "sentence_transformers": "5.5.1",
+    "transformers": "5.7.0"
+  },
+  "default_prompt_name": null,
+  "model_type": "SentenceTransformer",
+  "prompts": {
+    "document": "",
+    "query": ""
+  },
+  "similarity_fn_name": "cosine"
+}

sc40/model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e90e602bb2564a90a29f266112ccbe0111011b64bfae89155688973da17566c2
+size 436347736

sc40/modules.json

@@ -0,0 +1,14 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.base.modules.transformer.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.sentence_transformer.modules.pooling.Pooling"
+  }
+]

sc40/sentence_bert_config.json

@@ -0,0 +1,10 @@
+{
+    "transformer_task": "feature-extraction",
+    "modality_config": {
+        "text": {
+            "method": "forward",
+            "method_output_name": "last_hidden_state"
+        }
+    },
+    "module_output_name": "token_embeddings"
+}

sc40/tokenizer_config.json

@@ -0,0 +1,22 @@
+{
+  "backend": "tokenizers",
+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "is_local": true,
+  "local_files_only": false,
+  "mask_token": "[MASK]",
+  "max_length": 128,
+  "model_max_length": 128,
+  "pad_to_multiple_of": null,
+  "pad_token": "[PAD]",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "[SEP]",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}