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--- |
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base_model: gechim/metadata-cls-no-gov-8k-v3 |
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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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model-index: |
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- name: PhobertLexicalMeta-v2 |
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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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# PhobertLexicalMeta-v2 |
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This model is a fine-tuned version of [gechim/metadata-cls-no-gov-8k-v3](https://huggingface.co/gechim/metadata-cls-no-gov-8k-v3) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3926 |
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- Accuracy: 0.9062 |
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- F1: 0.8781 |
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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: 2e-05 |
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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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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:------:| |
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| No log | 0.8772 | 100 | 0.2699 | 0.9080 | 0.8801 | |
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| 0.1564 | 1.7544 | 200 | 0.2984 | 0.9011 | 0.8723 | |
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| 0.073 | 2.6316 | 300 | 0.3218 | 0.8987 | 0.8705 | |
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| 0.0502 | 3.5088 | 400 | 0.3472 | 0.8927 | 0.8641 | |
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| 0.0326 | 4.3860 | 500 | 0.3627 | 0.8941 | 0.8635 | |
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| 0.0285 | 5.2632 | 600 | 0.3752 | 0.8964 | 0.8685 | |
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| 0.0179 | 6.1404 | 700 | 0.3666 | 0.9025 | 0.8734 | |
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| 0.0156 | 7.0175 | 800 | 0.3759 | 0.9043 | 0.8748 | |
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| 0.0156 | 7.8947 | 900 | 0.3830 | 0.9080 | 0.8788 | |
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| 0.011 | 8.7719 | 1000 | 0.3917 | 0.9039 | 0.8746 | |
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| 0.0092 | 9.6491 | 1100 | 0.3926 | 0.9062 | 0.8781 | |
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### Framework versions |
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- Transformers 4.43.3 |
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- Pytorch 2.1.2 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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