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--- |
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base_model: SALT-NLP/FLANG-BERT |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: FLANG-BERT_flang-bert |
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results: [] |
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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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# FLANG-BERT_flang-bert |
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This model is a fine-tuned version of [SALT-NLP/FLANG-BERT](https://huggingface.co/SALT-NLP/FLANG-BERT) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4908 |
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- Accuracy: 0.8487 |
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- F1: 0.8486 |
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- Precision: 0.8489 |
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- Recall: 0.8487 |
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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: 0.0001 |
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- train_batch_size: 64 |
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- eval_batch_size: 64 |
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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_steps: 1000 |
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- num_epochs: 25 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| 0.8968 | 1.0 | 91 | 0.8147 | 0.6256 | 0.5775 | 0.6185 | 0.6256 | |
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| 0.5694 | 2.0 | 182 | 0.4893 | 0.8097 | 0.8101 | 0.8124 | 0.8097 | |
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| 0.3393 | 3.0 | 273 | 0.4303 | 0.8471 | 0.8472 | 0.8474 | 0.8471 | |
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| 0.2442 | 4.0 | 364 | 0.5042 | 0.8487 | 0.8462 | 0.8512 | 0.8487 | |
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| 0.1807 | 5.0 | 455 | 0.4908 | 0.8487 | 0.8486 | 0.8489 | 0.8487 | |
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| 0.1061 | 6.0 | 546 | 0.5168 | 0.8409 | 0.8396 | 0.8405 | 0.8409 | |
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| 0.1287 | 7.0 | 637 | 0.6537 | 0.8440 | 0.8438 | 0.8470 | 0.8440 | |
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| 0.1079 | 8.0 | 728 | 0.6641 | 0.8315 | 0.8319 | 0.8345 | 0.8315 | |
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| 0.0693 | 9.0 | 819 | 0.8833 | 0.8346 | 0.8344 | 0.8363 | 0.8346 | |
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| 0.1084 | 10.0 | 910 | 0.8721 | 0.7941 | 0.7947 | 0.7972 | 0.7941 | |
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### Framework versions |
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- Transformers 4.37.0 |
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- Pytorch 2.1.2 |
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- Datasets 2.1.0 |
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- Tokenizers 0.15.1 |
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