raveenatripathi
commited on
Commit
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Parent(s):
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Training in progress epoch 0
Browse files- README.md +55 -0
- config.json +113 -0
- special_tokens_map.json +7 -0
- tf_model.h5 +3 -0
- tokenizer.json +0 -0
- tokenizer_config.json +55 -0
- vocab.txt +0 -0
README.md
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---
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license: apache-2.0
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base_model: distilbert/distilbert-base-uncased
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tags:
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- generated_from_keras_callback
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model-index:
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- name: raveenatripathi/my_model
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results: []
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---
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<!-- This model card has been generated automatically according to the information Keras had access to. You should
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probably proofread and complete it, then remove this comment. -->
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# raveenatripathi/my_model
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This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Train Loss: 4.8226
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- Validation Loss: 3.2485
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- Train Accuracy: 0.3233
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- Epoch: 0
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 3435, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
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- training_precision: float32
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### Training results
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| Train Loss | Validation Loss | Train Accuracy | Epoch |
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|:----------:|:---------------:|:--------------:|:-----:|
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| 4.8226 | 3.2485 | 0.3233 | 0 |
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### Framework versions
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- Transformers 4.38.1
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- TensorFlow 2.15.0
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- Datasets 2.17.1
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- Tokenizers 0.15.2
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config.json
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{
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"_name_or_path": "distilbert/distilbert-base-uncased",
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"activation": "gelu",
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"architectures": [
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"DistilBertForSequenceClassification"
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],
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"attention_dropout": 0.1,
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"dim": 768,
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"dropout": 0.1,
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"hidden_dim": 3072,
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"id2label": {
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"0": "no_relation",
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"1": "org:alternate_names",
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"2": "org:city_of_headquarters",
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"3": "org:country_of_headquarters",
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"4": "org:dissolved",
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"5": "org:founded",
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"6": "org:founded_by",
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"7": "org:member_of",
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"8": "org:members",
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"9": "org:number_of_employees/members",
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"10": "org:parents",
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"11": "org:political/religious_affiliation",
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"12": "org:shareholders",
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"13": "org:stateorprovince_of_headquarters",
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"14": "org:subsidiaries",
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"15": "org:top_members/employees",
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"16": "org:website",
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"17": "per:age",
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"18": "per:alternate_names",
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"19": "per:cause_of_death",
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"20": "per:charges",
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"21": "per:children",
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"22": "per:cities_of_residence",
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"23": "per:city_of_birth",
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"24": "per:city_of_death",
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"25": "per:countries_of_residence",
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"26": "per:country_of_birth",
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"27": "per:country_of_death",
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"28": "per:date_of_birth",
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"29": "per:date_of_death",
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"30": "per:employee_of",
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"31": "per:origin",
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"32": "per:other_family",
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"33": "per:parents",
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"34": "per:religion",
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"35": "per:schools_attended",
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"36": "per:siblings",
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"37": "per:spouse",
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"38": "per:stateorprovince_of_birth",
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"39": "per:stateorprovince_of_death",
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"40": "per:stateorprovinces_of_residence",
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"41": "per:title",
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"42": "per:identity"
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},
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"initializer_range": 0.02,
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"label2id": {
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"no_relation": 0,
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"org:alternate_names": 1,
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"org:city_of_headquarters": 2,
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"org:country_of_headquarters": 3,
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"org:dissolved": 4,
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"org:founded": 5,
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"org:founded_by": 6,
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"org:member_of": 7,
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"org:members": 8,
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"org:number_of_employees/members": 9,
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"org:parents": 10,
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"org:political/religious_affiliation": 11,
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"org:shareholders": 12,
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"org:stateorprovince_of_headquarters": 13,
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"org:subsidiaries": 14,
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"org:top_members/employees": 15,
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"org:website": 16,
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"per:age": 17,
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"per:alternate_names": 18,
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"per:cause_of_death": 19,
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"per:charges": 20,
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"per:children": 21,
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"per:cities_of_residence": 22,
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"per:city_of_birth": 23,
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"per:city_of_death": 24,
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"per:countries_of_residence": 25,
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"per:country_of_birth": 26,
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"per:country_of_death": 27,
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"per:date_of_birth": 28,
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"per:date_of_death": 29,
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"per:employee_of": 30,
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"per:identity": 42,
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"per:origin": 31,
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"per:other_family": 32,
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"per:parents": 33,
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"per:religion": 34,
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"per:schools_attended": 35,
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"per:siblings": 36,
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"per:spouse": 37,
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"per:stateorprovince_of_birth": 38,
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"per:stateorprovince_of_death": 39,
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"per:stateorprovinces_of_residence": 40,
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"per:title": 41
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},
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"max_position_embeddings": 512,
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"model_type": "distilbert",
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"n_heads": 12,
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"n_layers": 6,
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"pad_token_id": 0,
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"qa_dropout": 0.1,
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"seq_classif_dropout": 0.2,
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"sinusoidal_pos_embds": false,
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"tie_weights_": true,
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"transformers_version": "4.38.1",
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"vocab_size": 30522
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}
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special_tokens_map.json
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{
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"cls_token": "[CLS]",
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"mask_token": "[MASK]",
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"unk_token": "[UNK]"
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}
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tf_model.h5
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version https://git-lfs.github.com/spec/v1
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oid sha256:c150a8465a44a10625a50ae95da59a5740dac8f510be90b3eaacd3420e3ae798
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size 268077760
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tokenizer.json
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See raw diff
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tokenizer_config.json
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{
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"added_tokens_decoder": {
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"0": {
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"content": "[PAD]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"100": {
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"content": "[UNK]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"101": {
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"content": "[CLS]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"102": {
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"content": "[SEP]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"103": {
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"content": "[MASK]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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}
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},
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"clean_up_tokenization_spaces": true,
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"cls_token": "[CLS]",
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"do_lower_case": true,
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"mask_token": "[MASK]",
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"model_max_length": 128,
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"strip_accents": null,
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"tokenize_chinese_chars": true,
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"tokenizer_class": "DistilBertTokenizer",
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"unk_token": "[UNK]"
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}
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vocab.txt
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