Training in progress, epoch 1
Browse files- README.md +90 -0
- all_results.json +17 -0
- config.json +37 -0
- eval_results.json +12 -0
- pytorch_model.bin +3 -0
- special_tokens_map.json +7 -0
- tokenizer.json +0 -0
- tokenizer_config.json +57 -0
- train_results.json +8 -0
- trainer_state.json +3886 -0
- training_args.bin +3 -0
- vocab.txt +0 -0
README.md
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---
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license: mit
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base_model: microsoft/xtremedistil-l6-h256-uncased
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tags:
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- generated_from_trainer
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datasets:
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- nbroad/company_names
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metrics:
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- precision
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- recall
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- f1
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- accuracy
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model-index:
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- name: xtremedistil-l6-h256-company-names
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results:
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- task:
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name: Token Classification
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type: token-classification
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dataset:
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name: nbroad/company_names
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type: nbroad/company_names
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metrics:
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- name: Precision
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type: precision
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value: 0.6998602375960866
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- name: Recall
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type: recall
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value: 0.7154210197339048
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- name: F1
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type: f1
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value: 0.7075550845586612
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- name: Accuracy
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type: accuracy
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value: 0.9702296390871982
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# xtremedistil-l6-h256-company-names
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This model is a fine-tuned version of [microsoft/xtremedistil-l6-h256-uncased](https://huggingface.co/microsoft/xtremedistil-l6-h256-uncased) on the nbroad/company_names dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0789
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- Precision: 0.6999
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- Recall: 0.7154
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- F1: 0.7076
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- Accuracy: 0.9702
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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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- learning_rate: 8e-05
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- train_batch_size: 48
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- eval_batch_size: 8
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_ratio: 0.1
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- num_epochs: 3.0
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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| 0.1052 | 1.0 | 2126 | 0.0854 | 0.6824 | 0.6605 | 0.6713 | 0.9678 |
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| 0.0724 | 2.0 | 4252 | 0.0814 | 0.6925 | 0.7042 | 0.6983 | 0.9696 |
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| 0.0778 | 3.0 | 6378 | 0.0789 | 0.6999 | 0.7154 | 0.7076 | 0.9702 |
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### Framework versions
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- Transformers 4.34.1
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- Pytorch 2.0.1+cu117
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- Datasets 2.16.1
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- Tokenizers 0.14.1
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all_results.json
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{
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"epoch": 3.0,
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"eval_accuracy": 0.9702296390871982,
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"eval_f1": 0.7075550845586612,
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"eval_loss": 0.07886078208684921,
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| 6 |
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"eval_precision": 0.6998602375960866,
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"eval_recall": 0.7154210197339048,
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"eval_runtime": 7.1372,
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"eval_samples": 14160,
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"eval_samples_per_second": 1983.973,
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"eval_steps_per_second": 247.997,
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"train_loss": 0.11718836608034722,
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| 13 |
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"train_runtime": 82.9226,
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| 14 |
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"train_samples": 102018,
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"train_samples_per_second": 3690.84,
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"train_steps_per_second": 76.915
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}
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config.json
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{
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"_name_or_path": "microsoft/xtremedistil-l6-h256-uncased",
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"architectures": [
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"BertForTokenClassification"
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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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"finetuning_task": "ner",
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"gradient_checkpointing": false,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 256,
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"id2label": {
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"0": "O",
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"1": "B-ORG",
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"2": "I-ORG"
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},
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"initializer_range": 0.02,
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"intermediate_size": 1024,
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"label2id": {
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"B-ORG": 1,
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"I-ORG": 2,
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"O": 0
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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": "bert",
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"num_attention_heads": 8,
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"num_hidden_layers": 6,
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| 30 |
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"pad_token_id": 0,
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"position_embedding_type": "absolute",
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"torch_dtype": "float32",
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"transformers_version": "4.34.1",
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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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eval_results.json
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{
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"epoch": 3.0,
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"eval_accuracy": 0.9702296390871982,
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| 4 |
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"eval_f1": 0.7075550845586612,
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| 5 |
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"eval_loss": 0.07886078208684921,
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| 6 |
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"eval_precision": 0.6998602375960866,
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| 7 |
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"eval_recall": 0.7154210197339048,
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| 8 |
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"eval_runtime": 7.1372,
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| 9 |
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"eval_samples": 14160,
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| 10 |
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"eval_samples_per_second": 1983.973,
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"eval_steps_per_second": 247.997
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}
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:0a60977d97d1ba123b47df5d56f1e5f1825e0672dbd1beba74222fd1a0f0c7d0
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size 50775017
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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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tokenizer.json
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The diff for this file is too large to render.
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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| 18 |
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},
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"101": {
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| 20 |
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"content": "[CLS]",
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| 21 |
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"lstrip": false,
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"normalized": false,
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| 23 |
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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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| 29 |
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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| 32 |
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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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| 37 |
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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| 40 |
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"single_word": false,
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"special": true
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| 42 |
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}
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},
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| 44 |
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"clean_up_tokenization_spaces": true,
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| 45 |
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"cls_token": "[CLS]",
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| 46 |
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"do_basic_tokenize": true,
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| 47 |
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"do_lower_case": true,
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| 48 |
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"mask_token": "[MASK]",
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| 49 |
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"model_max_length": 1000000000000000019884624838656,
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| 50 |
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"never_split": null,
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| 51 |
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"pad_token": "[PAD]",
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| 52 |
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"sep_token": "[SEP]",
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| 53 |
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"strip_accents": null,
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| 54 |
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"tokenize_chinese_chars": true,
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| 55 |
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"tokenizer_class": "BertTokenizer",
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| 56 |
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"unk_token": "[UNK]"
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| 57 |
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}
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train_results.json
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{
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"epoch": 3.0,
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| 3 |
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"train_loss": 0.11718836608034722,
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| 4 |
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"train_runtime": 82.9226,
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| 5 |
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"train_samples": 102018,
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| 6 |
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"train_samples_per_second": 3690.84,
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| 7 |
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"train_steps_per_second": 76.915
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| 8 |
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}
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trainer_state.json
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