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--- |
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license: mit |
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base_model: roberta-large |
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tags: |
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- generated_from_trainer |
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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: ner-gec-roberta-large-v4 |
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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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# ner-gec-roberta-large-v4 |
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This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2491 |
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- Precision: 0.6427 |
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- Recall: 0.5771 |
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- F1: 0.6081 |
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- Accuracy: 0.9614 |
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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: 64 |
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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: 10.0 |
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### Training results |
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| Training Loss | Epoch | Step | Accuracy | F1 | Validation Loss | Precision | Recall | |
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|:-------------:|:-----:|:----:|:--------:|:------:|:---------------:|:---------:|:------:| |
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| 0.2536 | 0.58 | 500 | 0.9347 | 0.0376 | 0.2469 | 0.0814 | 0.0245 | |
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| 0.2316 | 1.15 | 1000 | 0.9359 | 0.1365 | 0.2339 | 0.2272 | 0.0975 | |
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| 0.2175 | 1.73 | 1500 | 0.9392 | 0.1823 | 0.2172 | 0.2842 | 0.1342 | |
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| 0.1757 | 2.3 | 2000 | 0.9438 | 0.3123 | 0.1979 | 0.4011 | 0.2556 | |
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| 0.1682 | 2.88 | 2500 | 0.9502 | 0.3911 | 0.1817 | 0.4787 | 0.3307 | |
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| 0.121 | 3.46 | 3000 | 0.9537 | 0.4504 | 0.1753 | 0.5310 | 0.3910 | |
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| 0.0982 | 4.03 | 3500 | 0.9556 | 0.4980 | 0.1807 | 0.5606 | 0.4480 | |
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| 0.0858 | 4.61 | 4000 | 0.9577 | 0.5304 | 0.1732 | 0.5867 | 0.4839 | |
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| 0.0563 | 5.18 | 4500 | 0.1839 | 0.6007 | 0.5155 | 0.5548 | 0.9585 | |
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| 0.0586 | 5.76 | 5000 | 0.1804 | 0.6231 | 0.5237 | 0.5691 | 0.9605 | |
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| 0.0404 | 6.34 | 5500 | 0.1948 | 0.6214 | 0.5423 | 0.5792 | 0.9599 | |
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| 0.0397 | 6.91 | 6000 | 0.1994 | 0.6309 | 0.5458 | 0.5852 | 0.9610 | |
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| 0.0281 | 7.49 | 6500 | 0.2131 | 0.6345 | 0.5568 | 0.5931 | 0.9610 | |
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| 0.0182 | 8.06 | 7000 | 0.2249 | 0.6507 | 0.5649 | 0.6047 | 0.9625 | |
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| 0.0188 | 8.64 | 7500 | 0.2322 | 0.6413 | 0.5782 | 0.6081 | 0.9612 | |
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| 0.0123 | 9.22 | 8000 | 0.2473 | 0.6506 | 0.5777 | 0.6120 | 0.9622 | |
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| 0.0123 | 9.79 | 8500 | 0.2491 | 0.6427 | 0.5771 | 0.6081 | 0.9614 | |
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### Framework versions |
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- Transformers 4.36.2 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.0 |
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