Add new SentenceTransformer model

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": true,
+  "pooling_mode_mean_tokens": false,
+  "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
+- generated_from_trainer
+- dataset_size:53851
+- loss:MultipleNegativesRankingLoss
+base_model: BAAI/bge-base-en-v1.5
+widget:
+- source_sentence: A certain junior class has 1000 students and a certain senior class
+    has 900 students. Among these students, there are 60 siblings pairs each consisting
+    of 1 junior and 1 senior. If 1 student is to be selected at random from each class,
+    what is the probability that the 2 students selected will be a sibling pair?
+  sentences:
+  - Let's see Pick 60/1000 first Then we can only pick 1 other pair from the 800 So
+    total will be 60 / 900 *1000 Simplify and you get 2/30000
+  - To maximize number of hot dogs with 300$ Total number of hot dogs bought in 250-pack
+    = 22.95*13 =298.35$ Amount remaining = 300 - 298.35 = 1.65$ This amount is too
+    less to buy any 8- pack . Greatest number of hot dogs one can buy with 300 $ =
+    250*13 = 3250
+  - artificial leg
+- source_sentence: A stock trader originally bought 300 shares of stock from a company
+    at a total cost of m dollars. If each share was sold at 80% above the original
+    cost per share of stock, then interns of m for how many dollars was each share
+    sold?
+  sentences:
+  - Let Cost of 300 shares be $ 3000 So, Cost of 1 shares be $ 10 =>m/300 Selling
+    price per share = (100+80)/100 * m/300 Or, Selling price per share = 9/5 * m/300
+    => 9m/1500
+  - The prognostic value of p53 nuclear accumulation in gastric cancer is still unclear,
+    as shown by the discordant results still reported in the literature. In this study,
+    we evaluated the correlation between p53 accumulation and long-term survival of
+    patients resected for intestinal and diffuse-type gastric cancer. Eighty-three
+    patients with carcinoma of the intestinal type and 53 patients with carcinoma
+    of the diffuse type were included in the study. Immunohistochemical staining of
+    the paraffin sections was performed by using monoclonal antibody DO1; cases were
+    considered positive when nuclear immunostaining was observed in 10% or more of
+    the tumor cells. Prognostic significance of different variables was investigated
+    by univariate and multivariate analysis. p53 positivity was found in 51.8% of
+    intestinal-type and 50.9% of diffuse-type cases. No significant correlation between
+    the rate of p53 overexpression and age, sex, tumor location, tumor size, depth
+    of invasion, lymph node involvement, distant metastases, and surgical radicality
+    was found in the two groups of patients. A statistically significant difference
+    in survival rate was observed between p53-negative and p53-positive cases in the
+    intestinal type (P < .05), confirmed by multivariate analysis (P < .005; relative
+    risk = 3.09). On the contrary, no correlation with survival was found in diffuse-type
+    cases according to p53 overexpression.
+  - Many animal behaviors occur in a regular cycle. Two types of cyclic behaviors
+    are circadian rhythms and migration.
+- source_sentence: Are lactate levels in severe malarial anaemia associated with haemozoin-containing
+    neutrophils and low levels of IL-12?
+  sentences:
+  - Hyperlactataemia is often associated with a poor outcome in severe malaria in
+    African children. To unravel the complex pathophysiology of this condition the
+    relationship between plasma lactate levels, parasite density, pro- and anti-inflammatory
+    cytokines, and haemozoin-containing leucocytes was studied in children with severe
+    falciparum malarial anaemia. Twenty-six children with a primary diagnosis of severe
+    malarial anaemia with any asexual Plasmodium falciparum parasite density and Hb
+    < 5 g/dL were studied and the association of plasma lactate levels and haemozoin-containing
+    leucocytes, parasite density, pro- and anti-inflammatory cytokines was measured.
+    The same associations were measured in non-severe malaria controls (N = 60). Parasite
+    density was associated with lactate levels on admission (r = 0.56, P < 0.005).
+    Moreover, haemozoin-containing neutrophils and IL-12 were strongly associated
+    with plasma lactate levels, independently of parasite density (r = 0.60, P = 0.003
+    and r = -0.46, P = 0.02, respectively). These associations were not found in controls
+    with uncomplicated malarial anaemia.
+  - one of two female reproductive organs that produces eggs and secretes estrogen.
+  - hydrogen
+- source_sentence: Does phosphatidylethanol mediate its effects on the vascular endothelial
+    growth factor via HDL receptor in endothelial cells?
+  sentences:
+  - 'Patients having previous bariatric surgery are at risk for weight regain and
+    return of co-morbidities. If an anatomic basis for the failure is identified,
+    many surgeons advocate revision or conversion to a Roux-en-Y gastric bypass. The
+    aim of this study was to determine whether revisional bariatric surgery leads
+    to sufficient weight loss and co-morbidity remission. From 2005-2012, patients
+    undergoing revision were entered into a prospectively maintained database. Perioperative
+    outcomes, including complications, weight loss, and co-morbidity remission, were
+    examined for all patients with a history of a previous vertical banded gastroplasty
+    (VBG) or Roux-en-Y gastric bypass (RYGB). Twenty-two patients with a history of
+    RYGB and 56 with a history of VBG were identified. Following the revisional procedure,
+    the RYGB group experienced 35.8% excess weight loss (%EWL) and a 31.8% morbidity
+    rate. For the VBG group, patients experienced a 46.2% %EWL from their weight before
+    the revisional operation with a 51.8% morbidity rate. Co-morbidity remission rate
+    was excellent. Diabetes (VBG:100%, RYGB: 85.7%), gastroesophageal reflux disease
+    (VBG: 94.4%, RYGB: 80%), and hypertension (VBG: 74.2%, RYGB:60%) demonstrated
+    significant improvement.'
+  - 'Explanation: Let A, B, C represent their respective weights. Then, we have: A
+    + B + C = (45 x 3) = 135 …. (i) A + B = (40 x 2) = 80 …. (ii) B + C = (44 x 2)
+    = 88 ….(iii) Adding (ii) and (iii), we get: A + 2B + C = 168 …. (iv) Subtracting
+    (i) from (iv), we get : B = 33. B’s weight = 33 kg.'
+  - Previous epidemiological studies have shown that light to moderate alcohol consumption
+    has protective effects against coronary heart disease but the mechanisms of the
+    beneficial effect of alcohol are not known. Ethanol may increase high density
+    lipoprotein (HDL) cholesterol concentration, augment the reverse cholesterol transport,
+    or regulate growth factors or adhesion molecules. To study whether qualitative
+    changes in HDL phospholipids mediate part of the beneficial effects of alcohol
+    on atherosclerosis by HDL receptor, we investigated whether phosphatidylethanol
+    (PEth) in HDL particles affects the secretion of vascular endothelial growth factor
+    (VEGF) by a human scavenger receptor CD36 and LIMPII analog-I (CLA-1)-mediated
+    pathway. Human EA.hy 926 endothelial cells were incubated in the presence of native
+    HDL or PEth-HDL. VEGF concentration and CLA-1 protein expression were measured.
+    Human CLA-1 receptor-mediated mechanisms in endothelial cells were studied using
+    CLA-1 blocking antibody and protein kinase inhibitors. Phosphatidylethanol-containing
+    HDL particles caused a 6-fold increase in the expression of CLA-1 in endothelial
+    cells compared with the effect of native HDL. That emergent effect was mediated
+    mainly through protein kinase C and p44/42 mitogen-activated protein kinase pathways.
+    PEth increased the secretion of VEGF and that increase could be abolished by a
+    CLA-1 blocking antibody.
+- source_sentence: Said to go hand-in-hand with science, what evolves as new materials,
+    designs, and processes are invented?
+  sentences:
+  - Technology evolves as new materials, designs, and processes are invented.
+  - Technological design constraints may be physical or social.
+  - let x=44444444,then 44444445=x+1 88888885=2x-3 44444442=x-2 44444438=x-6 44444444^2=x^2
+    then substitute it in equation (x+1)(2x-3)(x-2)+(x-6)/x^2 ans is 2x-5 i.e 88888883
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# SentenceTransformer based on BAAI/bge-base-en-v1.5
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5). 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:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 768 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': 512, 'do_lower_case': True}) with Transformer model: BertModel 
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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("danthepol/MNLP_M3_document_encoder")
+# Run inference
+sentences = [
+    'Said to go hand-in-hand with science, what evolves as new materials, designs, and processes are invented?',
+    'Technology evolves as new materials, designs, and processes are invented.',
+    'Technological design constraints may be physical or social.',
+]
+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.*
+-->
+
+<!--
+## 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: 53,851 training samples
+* Columns: <code>sentence_0</code> and <code>sentence_1</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence_0                                                                         | sentence_1                                                                          |
+  |:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                              |
+  | details | <ul><li>min: 8 tokens</li><li>mean: 31.16 tokens</li><li>max: 143 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 160.39 tokens</li><li>max: 512 tokens</li></ul> |
+* Samples:
+  | sentence_0                                                                                                                                                                                                                                                                                                                                                                              | sentence_1                                                                                                                                                                                                                                                                                                                                                                                                                                                                               |
+  |:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>For integers U and V, when U is divided by V, the remainder is odd. Which of the following must be true?</code>                                                                                                                                                                                                                                                                   | <code>At least one of U and V is odd</code>                                                                                                                                                                                                                                                                                                                                                                                                                                              |
+  | <code>A mailman puts .05% of letters in the wrong mailbox. How many deliveries must he make to misdeliver 2 items?</code>                                                                                                                                                                                                                                                               | <code>Let the number of total deliveries be x Then, .05% of x=2 (5/100)*(1/100)*x=2 x=4000</code>                                                                                                                                                                                                                                                                                                                                                                                        |
+  | <code>A certain ball team has an equal number of right- and left-handed players. On a certain day, two-thirds of the players were absent from practice. Of the players at practice that day, two-third were left handed. What is the ratio of the number of right-handed players who were not at practice that day to the number of lefthanded players who were not at practice?</code> | <code>Say the total number of players is 18, 9 right-handed and 9 left-handed. On a certain day, two-thirds of the players were absent from practice --> 12 absent and 6 present. Of the players at practice that day, one-third were left-handed --> 6*2/3=4 were left-handed and 2 right-handed. The number of right-handed players who were not at practice that day is 9-2=7. The number of left-handed players who were not at practice that days is 9-4=5. The ratio = 7/5.</code> |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `per_device_train_batch_size`: 32
+- `per_device_eval_batch_size`: 32
+- `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`: 3
+- `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}
+- `tp_size`: 0
+- `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
+- `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`: batch_sampler
+- `multi_dataset_batch_sampler`: round_robin
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss |
+|:------:|:----:|:-------------:|
+| 0.2971 | 500  | 0.1286        |
+| 0.5942 | 1000 | 0.0769        |
+| 0.8913 | 1500 | 0.0682        |
+| 1.1884 | 2000 | 0.0416        |
+| 1.4854 | 2500 | 0.0369        |
+| 1.7825 | 3000 | 0.0326        |
+| 2.0796 | 3500 | 0.0331        |
+| 2.3767 | 4000 | 0.0213        |
+| 2.6738 | 4500 | 0.0211        |
+| 2.9709 | 5000 | 0.0207        |
+
+
+### Framework Versions
+- Python: 3.12.8
+- Sentence Transformers: 3.4.1
+- Transformers: 4.51.3
+- PyTorch: 2.5.1+cu124
+- Accelerate: 1.3.0
+- 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",
+}
+```
+
+#### 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 +1,31 @@
+{
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "LABEL_0"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "LABEL_0": 0
+  },
+  "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": "4.51.3",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.4.1",
+    "transformers": "4.51.3",
+    "pytorch": "2.5.1+cu124"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:70a7a73dac381f0340d2b4e33239d2419af70deca376afc1a8728446b9a4a09b
+size 437951328

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": 512,
+  "do_lower_case": true
+}

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": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "max_length": 512,
+  "model_max_length": 512,
+  "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]"
+}