Add new CrossEncoder model

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README.md +471 -0
config.json +55 -0
model.safetensors +3 -0
special_tokens_map.json +37 -0
tokenizer.json +0 -0
tokenizer_config.json +945 -0

README.md ADDED Viewed

	@@ -0,0 +1,471 @@

+---
+language:
+- en
+license: apache-2.0
+tags:
+- sentence-transformers
+- cross-encoder
+- reranker
+- generated_from_trainer
+- dataset_size:180144
+- loss:BinaryCrossEntropyLoss
+base_model: answerdotai/ModernBERT-base
+pipeline_tag: text-ranking
+library_name: sentence-transformers
+---
+# answerdotai/ModernBERT-base trained on schema JSONL
+This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) 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:** [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) <!-- at revision 8949b909ec900327062f0ebf497f51aef5e6f0c8 -->
+- **Maximum Sequence Length:** 8192 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("thanhdath/reranker-ModernBERT-base-schema-bce")
+# Get scores for pairs of texts
+pairs = [
+    ['Name movie titles released in year 1945. Sort the listing by the descending order of movie popularity.\nHint: released in the year 1945 refers to movie_release_year = 1945;', 'Column: movies.movie_popularity ; Column meaning: Number of Mubi users who love this movie ; Column type: INTEGER ; Column has values: "105" ; Column has null values: False'],
+    ['Name movie titles released in year 1945. Sort the listing by the descending order of movie popularity.\nHint: released in the year 1945 refers to movie_release_year = 1945;', 'Column: lists_users.user_has_payment_method ; Column meaning: user_has_payment_method ; Column type: TEXT ; Column has values: "1" ; Column has null values: False'],
+    ['Name movie titles released in year 1945. Sort the listing by the descending order of movie popularity.\nHint: released in the year 1945 refers to movie_release_year = 1945;', 'Column: lists.list_description ; Column meaning: List description made by the user ; Column type: TEXT ; Column has values: "<p>[sorted by the year released]</p>", "<p>Films sorted by release year.</p>" ; Column has null values: False'],
+    ['Name movie titles released in year 1945. Sort the listing by the descending order of movie popularity.\nHint: released in the year 1945 refers to movie_release_year = 1945;', 'Column: lists.list_second_image_url ; Column meaning: list_second_image_url ; Column type: TEXT ; Column has values:  ; Column has null values: False'],
+    ['Name movie titles released in year 1945. Sort the listing by the descending order of movie popularity.\nHint: released in the year 1945 refers to movie_release_year = 1945;', 'Column: movies.movie_release_year ; Column meaning: Release year of the movie ; Column type: INTEGER ; Column has values: "1945" ; Column has null values: False'],
+]
+scores = model.predict(pairs)
+print(scores.shape)
+# (5,)
+# Or rank different texts based on similarity to a single text
+ranks = model.rank(
+    'Name movie titles released in year 1945. Sort the listing by the descending order of movie popularity.\nHint: released in the year 1945 refers to movie_release_year = 1945;',
+    [
+        'Column: movies.movie_popularity ; Column meaning: Number of Mubi users who love this movie ; Column type: INTEGER ; Column has values: "105" ; Column has null values: False',
+        'Column: lists_users.user_has_payment_method ; Column meaning: user_has_payment_method ; Column type: TEXT ; Column has values: "1" ; Column has null values: False',
+        'Column: lists.list_description ; Column meaning: List description made by the user ; Column type: TEXT ; Column has values: "<p>[sorted by the year released]</p>", "<p>Films sorted by release year.</p>" ; Column has null values: False',
+        'Column: lists.list_second_image_url ; Column meaning: list_second_image_url ; Column type: TEXT ; Column has values:  ; Column has null values: False',
+        'Column: movies.movie_release_year ; Column meaning: Release year of the movie ; Column type: INTEGER ; Column has values: "1945" ; Column has null values: False',
+    ]
+)
+# [{'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.*
+-->
+<!--
+## 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: 180,144 training samples
+* Columns: <code>sentence_A</code>, <code>sentence_B</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence_A                                                                                       | sentence_B                                                                                        | label                                           |
+  |:--------|:-------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------|:------------------------------------------------|
+  | type    | string                                                                                           | string                                                                                            | int                                             |
+  | details | <ul><li>min: 95 characters</li><li>mean: 264.54 characters</li><li>max: 551 characters</li></ul> | <ul><li>min: 127 characters</li><li>mean: 177.59 characters</li><li>max: 313 characters</li></ul> | <ul><li>0: ~75.00%</li><li>1: ~25.00%</li></ul> |
+* Samples:
+  | sentence_A                                                                                                                                                                                  | sentence_B                                                                                                                                                                                                                                              | label          |
+  |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
+  | <code>Name movie titles released in year 1945. Sort the listing by the descending order of movie popularity.<br>Hint: released in the year 1945 refers to movie_release_year = 1945;</code> | <code>Column: movies.movie_popularity ; Column meaning: Number of Mubi users who love this movie ; Column type: INTEGER ; Column has values: "105" ; Column has null values: False</code>                                                               | <code>1</code> |
+  | <code>Name movie titles released in year 1945. Sort the listing by the descending order of movie popularity.<br>Hint: released in the year 1945 refers to movie_release_year = 1945;</code> | <code>Column: lists_users.user_has_payment_method ; Column meaning: user_has_payment_method ; Column type: TEXT ; Column has values: "1" ; Column has null values: False</code>                                                                         | <code>0</code> |
+  | <code>Name movie titles released in year 1945. Sort the listing by the descending order of movie popularity.<br>Hint: released in the year 1945 refers to movie_release_year = 1945;</code> | <code>Column: lists.list_description ; Column meaning: List description made by the user ; Column type: TEXT ; Column has values: "<p>[sorted by the year released]</p>", "<p>Films sorted by release year.</p>" ; Column has null values: False</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": 2.9947667121887207
+  }
+  ```
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+- `per_device_train_batch_size`: 128
+- `per_device_eval_batch_size`: 128
+- `learning_rate`: 1e-05
+- `weight_decay`: 0.01
+- `num_train_epochs`: 10
+- `warmup_ratio`: 0.1
+- `seed`: 12
+- `fp16`: True
+- `dataloader_num_workers`: 4
+#### 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`: 128
+- `per_device_eval_batch_size`: 128
+- `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`: 1e-05
+- `weight_decay`: 0.01
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 10
+- `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`: False
+- `fp16`: True
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 4
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: None
+- `hub_always_push`: False
+- `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`: proportional
+- `router_mapping`: {}
+- `learning_rate_mapping`: {}
+</details>
+### Training Logs
+<details><summary>Click to expand</summary>
+| Epoch  | Step  | Training Loss |
+|:------:|:-----:|:-------------:|
+| 0.0007 | 1     | 1.1598        |
+| 0.0710 | 100   | 1.0653        |
+| 0.1420 | 200   | 1.023         |
+| 0.2131 | 300   | 0.9502        |
+| 0.2841 | 400   | 0.8251        |
+| 0.3551 | 500   | 0.6595        |
+| 0.4261 | 600   | 0.5691        |
+| 0.4972 | 700   | 0.5061        |
+| 0.5682 | 800   | 0.4711        |
+| 0.6392 | 900   | 0.4209        |
+| 0.7102 | 1000  | 0.3882        |
+| 0.7812 | 1100  | 0.3778        |
+| 0.8523 | 1200  | 0.3743        |
+| 0.9233 | 1300  | 0.3248        |
+| 0.9943 | 1400  | 0.3283        |
+| 1.0653 | 1500  | 0.2973        |
+| 1.1364 | 1600  | 0.2653        |
+| 1.2074 | 1700  | 0.263         |
+| 1.2784 | 1800  | 0.2548        |
+| 1.3494 | 1900  | 0.2329        |
+| 1.4205 | 2000  | 0.2345        |
+| 1.4915 | 2100  | 0.2303        |
+| 1.5625 | 2200  | 0.205         |
+| 1.6335 | 2300  | 0.2077        |
+| 1.7045 | 2400  | 0.1836        |
+| 1.7756 | 2500  | 0.186         |
+| 1.8466 | 2600  | 0.1877        |
+| 1.9176 | 2700  | 0.1757        |
+| 1.9886 | 2800  | 0.1742        |
+| 2.0597 | 2900  | 0.1278        |
+| 2.1307 | 3000  | 0.104         |
+| 2.2017 | 3100  | 0.1135        |
+| 2.2727 | 3200  | 0.1087        |
+| 2.3438 | 3300  | 0.0998        |
+| 2.4148 | 3400  | 0.103         |
+| 2.4858 | 3500  | 0.1029        |
+| 2.5568 | 3600  | 0.096         |
+| 2.6278 | 3700  | 0.1021        |
+| 2.6989 | 3800  | 0.0836        |
+| 2.7699 | 3900  | 0.08          |
+| 2.8409 | 4000  | 0.0858        |
+| 2.9119 | 4100  | 0.0816        |
+| 2.9830 | 4200  | 0.0724        |
+| 3.0540 | 4300  | 0.0451        |
+| 3.125  | 4400  | 0.0415        |
+| 3.1960 | 4500  | 0.0396        |
+| 3.2670 | 4600  | 0.0397        |
+| 3.3381 | 4700  | 0.0406        |
+| 3.4091 | 4800  | 0.0468        |
+| 3.4801 | 4900  | 0.0395        |
+| 3.5511 | 5000  | 0.0399        |
+| 3.6222 | 5100  | 0.0498        |
+| 3.6932 | 5200  | 0.0453        |
+| 3.7642 | 5300  | 0.0376        |
+| 3.8352 | 5400  | 0.0472        |
+| 3.9062 | 5500  | 0.038         |
+| 3.9773 | 5600  | 0.0323        |
+| 4.0483 | 5700  | 0.0214        |
+| 4.1193 | 5800  | 0.0173        |
+| 4.1903 | 5900  | 0.0229        |
+| 4.2614 | 6000  | 0.0218        |
+| 4.3324 | 6100  | 0.0216        |
+| 4.4034 | 6200  | 0.0135        |
+| 4.4744 | 6300  | 0.015         |
+| 4.5455 | 6400  | 0.0204        |
+| 4.6165 | 6500  | 0.0201        |
+| 4.6875 | 6600  | 0.0145        |
+| 4.7585 | 6700  | 0.0146        |
+| 4.8295 | 6800  | 0.0191        |
+| 4.9006 | 6900  | 0.0204        |
+| 4.9716 | 7000  | 0.0129        |
+| 5.0426 | 7100  | 0.0158        |
+| 5.1136 | 7200  | 0.0045        |
+| 5.1847 | 7300  | 0.0033        |
+| 5.2557 | 7400  | 0.0041        |
+| 5.3267 | 7500  | 0.0082        |
+| 5.3977 | 7600  | 0.0129        |
+| 5.4688 | 7700  | 0.0055        |
+| 5.5398 | 7800  | 0.0047        |
+| 5.6108 | 7900  | 0.0076        |
+| 5.6818 | 8000  | 0.0085        |
+| 5.7528 | 8100  | 0.0129        |
+| 5.8239 | 8200  | 0.0089        |
+| 5.8949 | 8300  | 0.0074        |
+| 5.9659 | 8400  | 0.0075        |
+| 6.0369 | 8500  | 0.0061        |
+| 6.1080 | 8600  | 0.0025        |
+| 6.1790 | 8700  | 0.003         |
+| 6.25   | 8800  | 0.0055        |
+| 6.3210 | 8900  | 0.0048        |
+| 6.3920 | 9000  | 0.0036        |
+| 6.4631 | 9100  | 0.0052        |
+| 6.5341 | 9200  | 0.0014        |
+| 6.6051 | 9300  | 0.0045        |
+| 6.6761 | 9400  | 0.0022        |
+| 6.7472 | 9500  | 0.0043        |
+| 6.8182 | 9600  | 0.0036        |
+| 6.8892 | 9700  | 0.0062        |
+| 6.9602 | 9800  | 0.0059        |
+| 7.0312 | 9900  | 0.0018        |
+| 7.1023 | 10000 | 0.0029        |
+| 7.1733 | 10100 | 0.002         |
+| 7.2443 | 10200 | 0.004         |
+| 7.3153 | 10300 | 0.002         |
+| 7.3864 | 10400 | 0.0016        |
+| 7.4574 | 10500 | 0.0031        |
+| 7.5284 | 10600 | 0.0032        |
+| 7.5994 | 10700 | 0.0025        |
+| 7.6705 | 10800 | 0.0016        |
+| 7.7415 | 10900 | 0.0014        |
+| 7.8125 | 11000 | 0.0011        |
+| 7.8835 | 11100 | 0.0005        |
+| 7.9545 | 11200 | 0.0001        |
+| 8.0256 | 11300 | 0.0001        |
+| 8.0966 | 11400 | 0.0003        |
+| 8.1676 | 11500 | 0.0           |
+| 8.2386 | 11600 | 0.0021        |
+| 8.3097 | 11700 | 0.0001        |
+| 8.3807 | 11800 | 0.0002        |
+| 8.4517 | 11900 | 0.0002        |
+| 8.5227 | 12000 | 0.0027        |
+| 8.5938 | 12100 | 0.0           |
+| 8.6648 | 12200 | 0.0024        |
+| 8.7358 | 12300 | 0.0001        |
+| 8.8068 | 12400 | 0.0004        |
+| 8.8778 | 12500 | 0.0001        |
+| 8.9489 | 12600 | 0.0008        |
+| 9.0199 | 12700 | 0.0001        |
+| 9.0909 | 12800 | 0.0           |
+| 9.1619 | 12900 | 0.0           |
+| 9.2330 | 13000 | 0.0           |
+| 9.3040 | 13100 | 0.0           |
+| 9.375  | 13200 | 0.0           |
+| 9.4460 | 13300 | 0.0           |
+| 9.5170 | 13400 | 0.0           |
+| 9.5881 | 13500 | 0.0           |
+| 9.6591 | 13600 | 0.0           |
+| 9.7301 | 13700 | 0.0           |
+| 9.8011 | 13800 | 0.0           |
+| 9.8722 | 13900 | 0.0           |
+| 9.9432 | 14000 | 0.0021        |
+</details>
+### Framework Versions
+- Python: 3.10.14
+- Sentence Transformers: 5.1.0
+- Transformers: 4.55.2
+- PyTorch: 2.2.2+cu121
+- Accelerate: 1.10.0
+- Datasets: 4.0.0
+- Tokenizers: 0.21.2
+## 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",
+}
+```
+<!--
+## 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 ADDED Viewed

	@@ -0,0 +1,55 @@

+{
+  "architectures": [
+    "ModernBertForSequenceClassification"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 50281,
+  "classifier_activation": "gelu",
+  "classifier_bias": false,
+  "classifier_dropout": 0.0,
+  "classifier_pooling": "mean",
+  "cls_token_id": 50281,
+  "decoder_bias": true,
+  "deterministic_flash_attn": false,
+  "embedding_dropout": 0.0,
+  "eos_token_id": 50282,
+  "global_attn_every_n_layers": 3,
+  "global_rope_theta": 160000.0,
+  "gradient_checkpointing": false,
+  "hidden_activation": "gelu",
+  "hidden_size": 768,
+  "id2label": {
+    "0": "LABEL_0"
+  },
+  "initializer_cutoff_factor": 2.0,
+  "initializer_range": 0.02,
+  "intermediate_size": 1152,
+  "label2id": {
+    "LABEL_0": 0
+  },
+  "layer_norm_eps": 1e-05,
+  "local_attention": 128,
+  "local_rope_theta": 10000.0,
+  "max_position_embeddings": 8192,
+  "mlp_bias": false,
+  "mlp_dropout": 0.0,
+  "model_type": "modernbert",
+  "norm_bias": false,
+  "norm_eps": 1e-05,
+  "num_attention_heads": 12,
+  "num_hidden_layers": 22,
+  "pad_token_id": 50283,
+  "position_embedding_type": "absolute",
+  "repad_logits_with_grad": false,
+  "sentence_transformers": {
+    "activation_fn": "torch.nn.modules.activation.Sigmoid",
+    "version": "5.1.0"
+  },
+  "sep_token_id": 50282,
+  "sparse_pred_ignore_index": -100,
+  "sparse_prediction": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.55.2",
+  "vocab_size": 50368
+}

model.safetensors ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:90429e9194d27215488a4c13674916ffeeb0245c0f65f39d309c8a9dcec5a43d
+size 598436708

special_tokens_map.json ADDED Viewed

	@@ -0,0 +1,37 @@

+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
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+}

tokenizer.json ADDED Viewed

The diff for this file is too large to render. See raw diff

tokenizer_config.json ADDED Viewed

	@@ -0,0 +1,945 @@

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