Training in progress epoch 0
Browse files- README.md +55 -0
- added_tokens.json +7 -0
- config.json +117 -0
- special_tokens_map.json +7 -0
- tf_model.h5 +3 -0
- tokenizer.json +0 -0
- tokenizer_config.json +56 -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: google/electra-base-discriminator
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tags:
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- generated_from_keras_callback
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model-index:
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- name: vladjr/electra-teste
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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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# vladjr/electra-teste
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This model is a fine-tuned version of [google/electra-base-discriminator](https://huggingface.co/google/electra-base-discriminator) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Train Loss: 3.0564
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- Validation Loss: 1.7752
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- Train Accuracy: 0.8333
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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': 2100, '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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| 3.0564 | 1.7752 | 0.8333 | 0 |
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### Framework versions
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- Transformers 4.34.0
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- TensorFlow 2.13.0
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- Datasets 2.14.5
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- Tokenizers 0.14.1
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added_tokens.json
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{
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"[CLS]": 101,
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"[MASK]": 103,
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"[PAD]": 0,
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"[SEP]": 102,
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"[UNK]": 100
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}
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config.json
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{
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"_name_or_path": "google/electra-base-discriminator",
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"architectures": [
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"ElectraForSequenceClassification"
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],
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"attention_probs_dropout_prob": 0.1,
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"classifier_dropout": null,
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"embedding_size": 768,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 768,
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"id2label": {
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"0": "Alternative Investment",
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"1": "Appreciative Inquiry",
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"2": "Artificial Intelligence",
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"3": "Accounts Receivable",
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"4": "Annual Review",
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"5": "Applicant Tracking System",
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"6": "Automated Trading System",
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"7": "Career Advancement",
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"8": "Chartered Accountant",
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"9": "Customer Acquisition",
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"10": "Career Pathing",
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"11": "Commercial Paper",
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"12": "Cost Per Mille",
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"13": "Cost Per Million",
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"14": "Credit Risk Management",
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"15": "Customer Relationship Management",
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"16": "Call to Action",
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"17": "Commodity Trading Advisor",
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"18": "Diversity, Equity, and Inclusion",
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"19": "Dividend Equity Income",
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"20": "Earnings Report",
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"21": "Employee Relations",
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"22": "Email Verification Process",
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"23": "Employee Value Proposition",
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"24": "Executive Vice President",
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"25": "Marketing Automation",
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"26": "Mergers & Acquisitions",
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"27": "Management BuyOut",
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"28": "Management By Objectives",
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"29": "Performance Management",
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"30": "Portfolio Manager",
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"31": "Product Marketing",
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"32": "Performance Review",
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"33": "Public Relations",
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"34": "Remarketing Audience",
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"35": "Risk Assessment",
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"36": "Return on Investment",
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"37": "Return on Involvement",
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"38": "Recruitment Process Outsourcing",
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"39": "Request for Purchase Order",
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"41": "Small and Medium-sized Enterprises",
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"42": "Subject Matter Expert"
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},
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"label2id": {
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"Accounts Receivable": 3,
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"Alternative Investment": 0,
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"Annual Review": 4,
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"Applicant Tracking System": 5,
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"Appreciative Inquiry": 1,
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"Artificial Intelligence": 2,
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"Automated Trading System": 6,
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"Call to Action": 16,
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"Career Advancement": 7,
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"Career Pathing": 10,
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"Chartered Accountant": 8,
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"Commercial Paper": 11,
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"Commodity Trading Advisor": 17,
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"Cost Per Mille": 12,
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"Cost Per Million": 13,
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"Credit Risk Management": 14,
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"Customer Acquisition": 9,
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"Customer Relationship Management": 15,
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"Diversity, Equity, and Inclusion": 18,
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"Dividend Equity Income": 19,
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"Earnings Report": 20,
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"Email Verification Process": 22,
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"Employee Relations": 21,
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"Employee Value Proposition": 23,
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"Executive Vice President": 24,
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"Management BuyOut": 27,
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"Management By Objectives": 28,
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"Marketing Automation": 25,
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"Mergers & Acquisitions": 26,
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"Performance Management": 29,
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"Performance Review": 32,
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"Portfolio Manager": 30,
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"Product Marketing": 31,
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"Public Relations": 33,
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"Recruitment Process Outsourcing": 38,
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"Remarketing Audience": 34,
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"Request for Purchase Order": 39,
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"Return on Investment": 36,
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"Return on Involvement": 37,
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"Risk Assessment": 35,
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"Small and Medium-sized Enterprises": 41,
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"Subject Matter Expert": 42
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},
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"layer_norm_eps": 1e-12,
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"max_position_embeddings": 512,
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"model_type": "electra",
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"pad_token_id": 0,
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"position_embedding_type": "absolute",
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"summary_activation": "gelu",
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"summary_last_dropout": 0.1,
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"summary_type": "first",
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"summary_use_proj": true,
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"transformers_version": "4.34.0",
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"type_vocab_size": 2,
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"use_cache": true,
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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:b75868f5c43e4daaba2669d5b345dfc0f71e5926d08f19d5abf44a4c05870b55
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size 438347984
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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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"additional_special_tokens": [],
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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": 512,
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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": "ElectraTokenizer",
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"unk_token": "[UNK]"
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}
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vocab.txt
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