End of training
Browse files- README.md +77 -0
- special_tokens_map.json +7 -0
- tokenizer.json +0 -0
- tokenizer_config.json +13 -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: bert-base-uncased
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tags:
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- generated_from_trainer
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datasets:
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- banking77
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metrics:
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- f1
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model-index:
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- name: bert-base-banking77-pt2
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results:
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- task:
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name: Text Classification
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type: text-classification
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dataset:
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name: banking77
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type: banking77
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config: default
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split: test
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args: default
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metrics:
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- name: F1
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type: f1
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value: 0.9308363539280016
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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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# bert-base-banking77-pt2
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the banking77 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.2978
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- F1: 0.9308
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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: 5e-05
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- train_batch_size: 16
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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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- num_epochs: 3
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | F1 |
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|:-------------:|:-----:|:----:|:---------------:|:------:|
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| 1.2051 | 1.0 | 626 | 0.8669 | 0.8173 |
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| 0.3924 | 2.0 | 1252 | 0.3695 | 0.9156 |
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| 0.187 | 3.0 | 1878 | 0.2978 | 0.9308 |
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### Framework versions
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- Transformers 4.33.1
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- Pytorch 2.0.1
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- Datasets 2.14.5
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- Tokenizers 0.13.3
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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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tokenizer_config.json
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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": 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": "BertTokenizer",
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"unk_token": "[UNK]"
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}
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vocab.txt
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