Add new CrossEncoder model

README.md

@@ -0,0 +1,493 @@
+---
+language:
+- en
+license: apache-2.0
+tags:
+- sentence-transformers
+- cross-encoder
+- generated_from_trainer
+- dataset_size:2223773
+- loss:BinaryCrossEntropyLoss
+base_model: cross-encoder/ms-marco-MiniLM-L6-v2
+pipeline_tag: text-ranking
+library_name: sentence-transformers
+metrics:
+- map
+- mrr@10
+- ndcg@10
+model-index:
+- name: ModernBERT-base trained on GooAQ
+  results:
+  - task:
+      type: cross-encoder-reranking
+      name: Cross Encoder Reranking
+    dataset:
+      name: gooaq dev
+      type: gooaq-dev
+    metrics:
+    - type: map
+      value: 0.638
+      name: Map
+    - type: mrr@10
+      value: 0.6361
+      name: Mrr@10
+    - type: ndcg@10
+      value: 0.6822
+      name: Ndcg@10
+  - task:
+      type: cross-encoder-reranking
+      name: Cross Encoder Reranking
+    dataset:
+      name: NanoMSMARCO R100
+      type: NanoMSMARCO_R100
+    metrics:
+    - type: map
+      value: 0.5437
+      name: Map
+    - type: mrr@10
+      value: 0.5348
+      name: Mrr@10
+    - type: ndcg@10
+      value: 0.606
+      name: Ndcg@10
+  - task:
+      type: cross-encoder-reranking
+      name: Cross Encoder Reranking
+    dataset:
+      name: NanoNFCorpus R100
+      type: NanoNFCorpus_R100
+    metrics:
+    - type: map
+      value: 0.3885
+      name: Map
+    - type: mrr@10
+      value: 0.563
+      name: Mrr@10
+    - type: ndcg@10
+      value: 0.4077
+      name: Ndcg@10
+  - task:
+      type: cross-encoder-reranking
+      name: Cross Encoder Reranking
+    dataset:
+      name: NanoNQ R100
+      type: NanoNQ_R100
+    metrics:
+    - type: map
+      value: 0.4626
+      name: Map
+    - type: mrr@10
+      value: 0.4628
+      name: Mrr@10
+    - type: ndcg@10
+      value: 0.5091
+      name: Ndcg@10
+  - task:
+      type: cross-encoder-nano-beir
+      name: Cross Encoder Nano BEIR
+    dataset:
+      name: NanoBEIR R100 mean
+      type: NanoBEIR_R100_mean
+    metrics:
+    - type: map
+      value: 0.4649
+      name: Map
+    - type: mrr@10
+      value: 0.5202
+      name: Mrr@10
+    - type: ndcg@10
+      value: 0.5076
+      name: Ndcg@10
+---
+
+# ModernBERT-base trained on GooAQ
+
+This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [cross-encoder/ms-marco-MiniLM-L6-v2](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L6-v2) using the [sentence-transformers](https://www.SBERT.net) library. It computes scores for pairs of texts, which can be used for text reranking and semantic search.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Cross Encoder
+- **Base model:** [cross-encoder/ms-marco-MiniLM-L6-v2](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L6-v2) <!-- at revision fbf9045f293a58fa68636213c5e0cb8a2de5d45e -->
+- **Maximum Sequence Length:** 512 tokens
+- **Number of Output Labels:** 1 label
+<!-- - **Training Dataset:** Unknown -->
+- **Language:** en
+- **License:** apache-2.0
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Documentation:** [Cross Encoder Documentation](https://www.sbert.net/docs/cross_encoder/usage/usage.html)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Cross Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=cross-encoder)
+
+## 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 CrossEncoder
+
+# Download from the 🤗 Hub
+model = CrossEncoder("ayushexel/reranker-ms-marco-MiniLM-L6-v2-gooaq-bce")
+# Get scores for pairs of texts
+pairs = [
+    ['what does it mean when you get a sharp pain in your left arm?', 'Pain in the left arm A pain in your left arm could mean you have a bone or joint injury, a pinched nerve, or a problem with your heart. Read on to learn more about the causes of left arm pain and what symptoms could signal a serious problem.'],
+    ['what does it mean when you get a sharp pain in your left arm?', "In this Article Whether it's throbbing, aching, or sharp, everyone has been in pain. The uncomfortable sensation is a red flag. Pain in your armpit could mean that you've simply strained a muscle, which is eased with ice and rest. It could also be a sign of more serious conditions, like an infection or breast cancer."],
+    ['what does it mean when you get a sharp pain in your left arm?', 'Sharp: When you feel a sudden, intense spike of pain, that qualifies as “sharp.” Sharp pain may also fit the descriptors cutting and shooting. Stabbing: Like sharp pain, stabbing pain occurs suddenly and intensely. However, stabbing pain may fade and reoccur many times.'],
+    ['what does it mean when you get a sharp pain in your left arm?', 'Symptoms. A herniated disc in the neck can cause neck pain, radiating arm pain, shoulder pain, and numbness or tingling in the arm or hand. The quality and type of pain can vary from dull, aching, and difficult to localize to sharp, burning, and easy to pinpoint.'],
+    ['what does it mean when you get a sharp pain in your left arm?', 'Injuries or trauma to any part of the arm or shoulder, including bone fractures, joint dislocations, and muscle strains and sprains, are common causes of arm pain. Sometimes diseases that affect other organs in the body, like peripheral vascular disease or arthritis, can be the cause of pain in the arm.'],
+]
+scores = model.predict(pairs)
+print(scores.shape)
+# (5,)
+
+# Or rank different texts based on similarity to a single text
+ranks = model.rank(
+    'what does it mean when you get a sharp pain in your left arm?',
+    [
+        'Pain in the left arm A pain in your left arm could mean you have a bone or joint injury, a pinched nerve, or a problem with your heart. Read on to learn more about the causes of left arm pain and what symptoms could signal a serious problem.',
+        "In this Article Whether it's throbbing, aching, or sharp, everyone has been in pain. The uncomfortable sensation is a red flag. Pain in your armpit could mean that you've simply strained a muscle, which is eased with ice and rest. It could also be a sign of more serious conditions, like an infection or breast cancer.",
+        'Sharp: When you feel a sudden, intense spike of pain, that qualifies as “sharp.” Sharp pain may also fit the descriptors cutting and shooting. Stabbing: Like sharp pain, stabbing pain occurs suddenly and intensely. However, stabbing pain may fade and reoccur many times.',
+        'Symptoms. A herniated disc in the neck can cause neck pain, radiating arm pain, shoulder pain, and numbness or tingling in the arm or hand. The quality and type of pain can vary from dull, aching, and difficult to localize to sharp, burning, and easy to pinpoint.',
+        'Injuries or trauma to any part of the arm or shoulder, including bone fractures, joint dislocations, and muscle strains and sprains, are common causes of arm pain. Sometimes diseases that affect other organs in the body, like peripheral vascular disease or arthritis, can be the cause of pain in the arm.',
+    ]
+)
+# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]
+```
+
+<!--
+### 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
+
+#### Cross Encoder Reranking
+
+* Dataset: `gooaq-dev`
+* Evaluated with [<code>CrossEncoderRerankingEvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderRerankingEvaluator) with these parameters:
+  ```json
+  {
+      "at_k": 10,
+      "always_rerank_positives": false
+  }
+  ```
+
+| Metric      | Value                |
+|:------------|:---------------------|
+| map         | 0.6380 (+0.2121)     |
+| mrr@10      | 0.6361 (+0.2199)     |
+| **ndcg@10** | **0.6822 (+0.2001)** |
+
+#### Cross Encoder Reranking
+
+* Datasets: `NanoMSMARCO_R100`, `NanoNFCorpus_R100` and `NanoNQ_R100`
+* Evaluated with [<code>CrossEncoderRerankingEvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderRerankingEvaluator) with these parameters:
+  ```json
+  {
+      "at_k": 10,
+      "always_rerank_positives": true
+  }
+  ```
+
+| Metric      | NanoMSMARCO_R100     | NanoNFCorpus_R100    | NanoNQ_R100          |
+|:------------|:---------------------|:---------------------|:---------------------|
+| map         | 0.5437 (+0.0541)     | 0.3885 (+0.1275)     | 0.4626 (+0.0430)     |
+| mrr@10      | 0.5348 (+0.0573)     | 0.5630 (+0.0632)     | 0.4628 (+0.0361)     |
+| **ndcg@10** | **0.6060 (+0.0655)** | **0.4077 (+0.0827)** | **0.5091 (+0.0084)** |
+
+#### Cross Encoder Nano BEIR
+
+* Dataset: `NanoBEIR_R100_mean`
+* Evaluated with [<code>CrossEncoderNanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderNanoBEIREvaluator) with these parameters:
+  ```json
+  {
+      "dataset_names": [
+          "msmarco",
+          "nfcorpus",
+          "nq"
+      ],
+      "rerank_k": 100,
+      "at_k": 10,
+      "always_rerank_positives": true
+  }
+  ```
+
+| Metric      | Value                |
+|:------------|:---------------------|
+| map         | 0.4649 (+0.0749)     |
+| mrr@10      | 0.5202 (+0.0522)     |
+| **ndcg@10** | **0.5076 (+0.0522)** |
+
+<!--
+## 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: 2,223,773 training samples
+* Columns: <code>question</code>, <code>answer</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | question                                                                                       | answer                                                                                           | label                                           |
+  |:--------|:-----------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|:------------------------------------------------|
+  | type    | string                                                                                         | string                                                                                           | int                                             |
+  | details | <ul><li>min: 19 characters</li><li>mean: 45.87 characters</li><li>max: 88 characters</li></ul> | <ul><li>min: 61 characters</li><li>mean: 253.13 characters</li><li>max: 374 characters</li></ul> | <ul><li>0: ~86.70%</li><li>1: ~13.30%</li></ul> |
+* Samples:
+  | question                                                                   | answer                                                                                                                                                                                                                                                                                                                                      | label          |
+  |:---------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
+  | <code>what does it mean when you get a sharp pain in your left arm?</code> | <code>Pain in the left arm A pain in your left arm could mean you have a bone or joint injury, a pinched nerve, or a problem with your heart. Read on to learn more about the causes of left arm pain and what symptoms could signal a serious problem.</code>                                                                              | <code>1</code> |
+  | <code>what does it mean when you get a sharp pain in your left arm?</code> | <code>In this Article Whether it's throbbing, aching, or sharp, everyone has been in pain. The uncomfortable sensation is a red flag. Pain in your armpit could mean that you've simply strained a muscle, which is eased with ice and rest. It could also be a sign of more serious conditions, like an infection or breast cancer.</code> | <code>0</code> |
+  | <code>what does it mean when you get a sharp pain in your left arm?</code> | <code>Sharp: When you feel a sudden, intense spike of pain, that qualifies as “sharp.” Sharp pain may also fit the descriptors cutting and shooting. Stabbing: Like sharp pain, stabbing pain occurs suddenly and intensely. However, stabbing pain may fade and reoccur many times.</code>                                                 | <code>0</code> |
+* Loss: [<code>BinaryCrossEntropyLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#binarycrossentropyloss) with these parameters:
+  ```json
+  {
+      "activation_fn": "torch.nn.modules.linear.Identity",
+      "pos_weight": 7
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 2048
+- `per_device_eval_batch_size`: 2048
+- `learning_rate`: 2e-05
+- `warmup_ratio`: 0.1
+- `seed`: 12
+- `bf16`: True
+- `dataloader_num_workers`: 12
+- `load_best_model_at_end`: True
+
+#### 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`: 2048
+- `per_device_eval_batch_size`: 2048
+- `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`: 2e-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`: 3
+- `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`: 12
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: True
+- `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`: 12
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: True
+- `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
+- `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`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch      | Step     | Training Loss | gooaq-dev_ndcg@10    | NanoMSMARCO_R100_ndcg@10 | NanoNFCorpus_R100_ndcg@10 | NanoNQ_R100_ndcg@10  | NanoBEIR_R100_mean_ndcg@10 |
+|:----------:|:--------:|:-------------:|:--------------------:|:------------------------:|:-------------------------:|:--------------------:|:--------------------------:|
+| -1         | -1       | -             | 0.6371 (+0.1550)     | 0.6686 (+0.1282)         | 0.3930 (+0.0680)          | 0.7599 (+0.2592)     | 0.6072 (+0.1518)           |
+| 0.0009     | 1        | 2.1175        | -                    | -                        | -                         | -                    | -                          |
+| 0.1842     | 200      | 1.1892        | -                    | -                        | -                         | -                    | -                          |
+| 0.3683     | 400      | 0.676         | -                    | -                        | -                         | -                    | -                          |
+| 0.5525     | 600      | 0.6268        | -                    | -                        | -                         | -                    | -                          |
+| 0.7366     | 800      | 0.606         | -                    | -                        | -                         | -                    | -                          |
+| 0.9208     | 1000     | 0.5933        | 0.6731 (+0.1910)     | 0.6038 (+0.0634)         | 0.4572 (+0.1321)          | 0.5220 (+0.0213)     | 0.5277 (+0.0723)           |
+| 1.1050     | 1200     | 0.5756        | -                    | -                        | -                         | -                    | -                          |
+| 1.2891     | 1400     | 0.5625        | -                    | -                        | -                         | -                    | -                          |
+| 1.4733     | 1600     | 0.5575        | -                    | -                        | -                         | -                    | -                          |
+| 1.6575     | 1800     | 0.549         | -                    | -                        | -                         | -                    | -                          |
+| 1.8416     | 2000     | 0.5475        | 0.6799 (+0.1977)     | 0.6072 (+0.0667)         | 0.4278 (+0.1028)          | 0.5031 (+0.0024)     | 0.5127 (+0.0573)           |
+| 2.0258     | 2200     | 0.5391        | -                    | -                        | -                         | -                    | -                          |
+| 2.2099     | 2400     | 0.5276        | -                    | -                        | -                         | -                    | -                          |
+| 2.3941     | 2600     | 0.5271        | -                    | -                        | -                         | -                    | -                          |
+| 2.5783     | 2800     | 0.5264        | -                    | -                        | -                         | -                    | -                          |
+| **2.7624** | **3000** | **0.5244**    | **0.6822 (+0.2001)** | **0.6060 (+0.0655)**     | **0.4077 (+0.0827)**      | **0.5091 (+0.0084)** | **0.5076 (+0.0522)**       |
+| 2.9466     | 3200     | 0.5235        | -                    | -                        | -                         | -                    | -                          |
+| -1         | -1       | -             | 0.6822 (+0.2001)     | 0.6060 (+0.0655)         | 0.4077 (+0.0827)          | 0.5091 (+0.0084)     | 0.5076 (+0.0522)           |
+
+* The bold row denotes the saved checkpoint.
+
+### Framework Versions
+- Python: 3.11.0
+- Sentence Transformers: 4.0.1
+- Transformers: 4.50.3
+- PyTorch: 2.6.0+cu124
+- Accelerate: 1.5.2
+- Datasets: 3.5.0
+- Tokenizers: 0.21.1
+
+## 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 +1,35 @@
+{
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "id2label": {
+    "0": "LABEL_0"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "label2id": {
+    "LABEL_0": 0
+  },
+  "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",
+  "sentence_transformers": {
+    "activation_fn": "torch.nn.modules.linear.Identity",
+    "version": "4.0.1"
+  },
+  "torch_dtype": "float32",
+  "transformers_version": "4.50.3",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:45ab940315e00147cc4d993886eb469cda5b4c69cad5d38dc1738504eefe0e6d
+size 90866412

special_tokens_map.json

@@ -0,0 +1,7 @@
+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

@@ -0,0 +1,58 @@
+{
+  "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,
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+    },
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+    },
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+    },
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+      "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]",
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}