Add new SentenceTransformer model

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

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+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- dense
+- generated_from_trainer
+- dataset_size:268
+- loss:MultipleNegativesRankingLoss
+base_model: sentence-transformers/all-MiniLM-L6-v2
+widget:
+- source_sentence: Birdwatching for Beginners with Barbara Hannah Grufferman
+  sentences:
+  - Bird that breeds in the Arctic and sub-Arctic and migrates to the Antarctic
+  - '[birds chirping] [Discover Bird-Watching
+
+    with Barbara Hannah Grufferman] [♪ music and birds chirping ♪] We are in the park
+    and I''m meeting up
+
+    with Birder Bob, who''s an expert on birding. >> It''s so nice to meet you.
+
+    >> Yes. So, listen. I came prepared.
+
+    I have my backpack. >> I even have a little notepad in there.
+
+    >> All right. >> But what am I missing?
+
+    >> Ah! Binoculars. >> May I place these over your head?
+
+    >> Please do! Thank you. [laughing]
+
+    Let''s go! Vamos! So birding is becoming
+
+    the fastest-growing outdoor activity— ["Birding Bob" DeCandido, Ornithologist]
+
+    >> Yes.
+
+    >> —in the country. >> Why do you think that is? Why?
+
+    >> Well, you can watch birds from inside looking outside
+
+    at a bird feeder in your backyard, but you can also go to a local park. [♪ music
+    ♪]
+
+    Here we are in this giant woodland in the middle of the city. >> And it''s beautiful.
+
+    >> Yeah. And we''re getting the clean air. We''re looking up.
+
+    We can see birds up there. >> We can hear the cardinals singing.
+
+    >> Here''s one. They''re migrating north along a flyway here. >> What''s a flyway?
+
+    >> Oh my goodness! A flyway is like an aerial path for birds, and oftentimes it''s
+    tied to a coastline
+
+    or a mountain chain. So there are some very common birds around
+
+    that are easy to recognize. Here he is.
+
+    Here''s your red-bellied woodpecker right here. I''m going to use my binoculars.
+
+    [laughing] Ah! It seems to me that with birding you could just—depending upon
+    weather— put on a sweater, a jacket, whatever and get out there and walk and look
+
+    and you''ll be birding. >> Yes.
+
+    >> Is it more complicated than that? Do I need more equipment? If you want to
+    take it to the next level,
+
+    a pair of inexpensive binoculars and a book so you have a reference to go with.
+    It''s like a guidebook to birds. Yes, because this is your classroom, you know?
+    >> Right.
+
+    >> And if you can teach yourself, all the best way in the world to learn. [♪ music
+    ♪] I''m going to do some special sounds. This is called pishing, which is
+
+    [demonstrating pishing] There comes somebody on the left. Now, it seems counterintuitive
+
+    that you make sounds and birds come to the sound. >> Yes.
+
+    >> But birds come in because they operate as a team. [demonstrating pishing]
+
+    What a wonderful thing! Yes, yeah.
+
+    [pishing] >> Look, here comes something.
+
+    >> You never know what you''re going to find
+
+    as you turn a corner. And all you need
+
+    is your eyes and ears and curiosity. Should I go closer? [birds chirping] Oh!
+
+    [bird chirping] Hello, little cutie! [birds chirping] They like my chia energy
+    bars.
+
+    [laughing] [♪ music and birds chirping ♪] [♪ music and birds chirping ♪] This
+    is such a great way to get outside,
+
+    move your body, and be with nature. Bird-watching is a great way
+
+    to see the local area and then take it national. I loved my birding experience
+    today. It’s—a new world
+
+    has been opened up for me really. So I think as of today
+
+    I can call myself an official birder. [AARP, Real Possibilities]'
+  - Teal is a dark cyan color. Its name comes from that of a bird, the Eurasian teal
+    which has a similarly colored stripe on its head. The word is often used colloquially
+    to refer to shades of cyan in general.
+- source_sentence: Corn bunting
+  sentences:
+  - The corn bunting is a passerine bird in the bunting family Emberizidae, a group
+    now separated by most modern authors from the finches, Fringillidae. This is a
+    large bunting with heavily streaked buff-brown plumage. The sexes are similar
+    but the male is slightly larger than the female. Its range extends from Western
+    Europe and North Africa across to northwestern China.
+  - The alpine swift is a species of swift found in Africa, southern Europe, and Asia.
+    They breed in mountains from southern Europe to the Himalayas. Like common swifts,
+    they are migratory; the southern European population winters further south in
+    southern Africa. They have very short legs which are used for clinging to vertical
+    surfaces. Like most swifts, they never settle voluntarily on the ground, spending
+    most of their lives in the air living on the insects they catch in their beaks.
+  - The little tern is a seabird of the family Laridae. It was first described by
+    the German naturalist Peter Simon Pallas in 1764 and given the binomial name Sterna
+    albifrons. It was moved to the genus Sternula when the genus Sterna was restricted
+    to the larger typical terns. The genus name Sternula is a diminutive of Sterna,
+    'tern', while the specific name albifrons is from Latin albus, 'white', and frons,
+    'forehead'.
+- source_sentence: Lesser spotted woodpecker
+  sentences:
+  - The Mediterranean gull is a small gull. The scientific name is from Ancient Greek.
+    The genus Ichthyaetus is from ikhthus, "fish", and aetos, "eagle", and the specific
+    melanocephalus is from melas, "black", and -kephalos "-headed".
+  - The spotted flycatcher is a small passerine bird in the Old World flycatcher family.
+    It breeds in most of Europe and in the Palearctic to Siberia, and is migratory,
+    wintering in Africa and south western Asia. It is declining in parts of its range.
+  - The lesser spotted woodpecker is a member of the woodpecker family Picidae. It
+    was formerly assigned to the genus Dendrocopos. Some taxonomic authorities continue
+    to list the species there.
+- source_sentence: Barnacle goose
+  sentences:
+  - The short-toed treecreeper is a small passerine bird found in woodlands through
+    much of the warmer regions of Europe and into north Africa. It has a generally
+    more southerly distribution than the other European treecreeper species, the common
+    treecreeper, with which it is easily confused where they both occur. The short-toed
+    treecreeper tends to prefer deciduous trees and lower altitudes than its relative
+    in these overlap areas. Although mainly sedentary, vagrants have occurred outside
+    the breeding range.
+  - The barnacle goose is a species of goose that belongs to the genus Branta of black
+    geese, which contains species with extensive black in the plumage, distinguishing
+    them from the grey Anser species. Despite its superficial similarity to the brant
+    goose, genetic analysis has shown its closest relative is the cackling goose.
+  - The grey plover or black-bellied plover is a large plover breeding in Arctic regions.
+    It is a long-distance migrant, with a nearly worldwide coastal distribution when
+    not breeding.
+- source_sentence: White stork
+  sentences:
+  - The long-tailed duck is a medium-sized sea duck that breeds in the tundra and
+    taiga regions of the arctic and winters along the northern coastlines of the Atlantic
+    and Pacific Oceans. It is the only member of the genus Clangula.
+  - The white stork is a large bird in the stork family, Ciconiidae. Its plumage is
+    mainly white, with black on the bird's wings. Adults have long red legs and long
+    pointed red beaks, and measure on average 100–115 cm (39–45 in) from beak tip
+    to end of tail, with a 155–215 cm (61–85 in) wingspan. The two subspecies, which
+    differ slightly in size, breed in Europe north to Finland, northwestern Africa,
+    Palearctic east to southern Kazakhstan and southern Africa. The white stork is
+    a long-distance migrant, wintering in Africa from tropical Sub-Saharan Africa
+    to as far south as South Africa, or on the Indian subcontinent. When migrating
+    between Europe and Africa, it avoids crossing the Mediterranean Sea and detours
+    via the Levant in the east or the Strait of Gibraltar in the west, because the
+    air thermals on which it depends for soaring do not form over water.
+  - 'The shovelers are four species of dabbling ducks in the genus Spatula with long,
+    broad spatula-shaped beaks:Red shoveler Spatula platalea
+
+    Cape shoveler Spatula smithii
+
+    Australasian shoveler Spatula rhynchotis
+
+    Northern shoveler Spatula clypeata'
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2). 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/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision c9745ed1d9f207416be6d2e6f8de32d1f16199bf -->
+- **Maximum Sequence Length:** 256 tokens
+- **Output Dimensionality:** 384 dimensions
+- **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': 256, 'do_lower_case': False, 'architecture': '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})
+  (2): Normalize()
+)
+```
+
+## 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("Nicolas-Spettel/bird-qa-model")
+# Run inference
+sentences = [
+    'White stork',
+    "The white stork is a large bird in the stork family, Ciconiidae. Its plumage is mainly white, with black on the bird's wings. Adults have long red legs and long pointed red beaks, and measure on average 100–115\xa0cm (39–45\xa0in) from beak tip to end of tail, with a 155–215\xa0cm (61–85\xa0in) wingspan. The two subspecies, which differ slightly in size, breed in Europe north to Finland, northwestern Africa, Palearctic east to southern Kazakhstan and southern Africa. The white stork is a long-distance migrant, wintering in Africa from tropical Sub-Saharan Africa to as far south as South Africa, or on the Indian subcontinent. When migrating between Europe and Africa, it avoids crossing the Mediterranean Sea and detours via the Levant in the east or the Strait of Gibraltar in the west, because the air thermals on which it depends for soaring do not form over water.",
+    'The long-tailed duck is a medium-sized sea duck that breeds in the tundra and taiga regions of the arctic and winters along the northern coastlines of the Atlantic and Pacific Oceans. It is the only member of the genus Clangula.',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 384]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities)
+# tensor([[1.0000, 0.7092, 0.0837],
+#         [0.7092, 1.0000, 0.1957],
+#         [0.0837, 0.1957, 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.*
+-->
+
+<!--
+## 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: 268 training samples
+* Columns: <code>sentence_0</code> and <code>sentence_1</code>
+* Approximate statistics based on the first 268 samples:
+  |         | sentence_0                                                                       | sentence_1                                                                          |
+  |:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
+  | type    | string                                                                           | string                                                                              |
+  | details | <ul><li>min: 3 tokens</li><li>mean: 5.63 tokens</li><li>max: 16 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 94.23 tokens</li><li>max: 256 tokens</li></ul> |
+* Samples:
+  | sentence_0                | sentence_1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        |
+  |:--------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>Corn bunting</code> | <code>The corn bunting is a passerine bird in the bunting family Emberizidae, a group now separated by most modern authors from the finches, Fringillidae. This is a large bunting with heavily streaked buff-brown plumage. The sexes are similar but the male is slightly larger than the female. Its range extends from Western Europe and North Africa across to northwestern China.</code>                                                                                                                                                                                                                   |
+  | <code>Water pipit</code>  | <code>The water pipit is a small passerine bird which breeds in the mountains of Southern Europe and the Palearctic eastwards to China. It is a short-distance migrant; many birds move to lower altitudes or wet open lowlands in winter.</code>                                                                                                                                                                                                                                                                                                                                                                 |
+  | <code>Marsh tit</code>    | <code>The marsh tit is a Eurasian passerine bird in the tit family Paridae and genus Poecile, closely related to the willow tit, Père David's and Songar tits. It is a small bird, around 12 cm (4.7 in) long and weighing 12 g (0.42 oz), with a black crown and nape, pale cheeks, brown back and greyish-brown wings and tail. Between 8 and 11 subspecies are recognised. Its close resemblance to the willow tit can cause identification problems, especially in the United Kingdom where the local subspecies of the two are very similar: they were not recognised as separate species until 1897.</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",
+      "gather_across_devices": false
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `per_device_train_batch_size`: 32
+- `per_device_eval_batch_size`: 32
+- `num_train_epochs`: 2
+- `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`: 32
+- `per_device_eval_batch_size`: 32
+- `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
+- `num_train_epochs`: 2
+- `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}
+- `parallelism_config`: None
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch_fused
+- `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
+- `hub_revision`: None
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `include_for_metrics`: []
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `use_liger_kernel`: False
+- `liger_kernel_config`: None
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: False
+- `prompts`: None
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: round_robin
+- `router_mapping`: {}
+- `learning_rate_mapping`: {}
+
+</details>
+
+### Framework Versions
+- Python: 3.13.7
+- Sentence Transformers: 5.1.0
+- Transformers: 4.56.1
+- PyTorch: 2.8.0+cpu
+- Accelerate: 1.10.1
+- Datasets: 4.0.0
+- Tokenizers: 0.22.0
+
+## 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",
+}
+```
+
+#### 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 +1,25 @@
+{
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "dtype": "float32",
+  "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": 6,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "transformers_version": "4.56.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,14 @@
+{
+  "__version__": {
+    "sentence_transformers": "5.1.0",
+    "transformers": "4.56.1",
+    "pytorch": "2.8.0+cpu"
+  },
+  "model_type": "SentenceTransformer",
+  "prompts": {
+    "query": "",
+    "document": ""
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2d5e25bbe0ea9982f67e9a42d15d484fb584c72c885b50484a54679f3b869a77
+size 90864192

modules.json

@@ -0,0 +1,20 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+    "max_seq_length": 256,
+    "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,37 @@
+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,65 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "max_length": 128,
+  "model_max_length": 256,
+  "never_split": null,
+  "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]"
+}