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--- |
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base_model: gyr66/RoBERTa-ext-large-chinese-finetuned-ner |
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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: RoBERTa-ext-large-lora-updated-chinese-finetuned-ner |
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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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# RoBERTa-ext-large-lora-updated-chinese-finetuned-ner |
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This model is a fine-tuned version of [gyr66/RoBERTa-ext-large-chinese-finetuned-ner](https://huggingface.co/gyr66/RoBERTa-ext-large-chinese-finetuned-ner) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.9586 |
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- Precision: 0.7016 |
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- Recall: 0.7518 |
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- F1: 0.7258 |
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- Accuracy: 0.9154 |
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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: 0.001 |
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- train_batch_size: 8 |
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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 |
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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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| 0.0034 | 1.0 | 252 | 1.0787 | 0.6753 | 0.7523 | 0.7117 | 0.9121 | |
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| 0.0032 | 2.0 | 504 | 1.0376 | 0.6830 | 0.7490 | 0.7145 | 0.9141 | |
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| 0.0018 | 3.0 | 756 | 1.0547 | 0.6731 | 0.7573 | 0.7127 | 0.9126 | |
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| 0.0032 | 4.0 | 1008 | 1.0262 | 0.6829 | 0.7384 | 0.7096 | 0.9126 | |
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| 0.0027 | 5.0 | 1260 | 0.9613 | 0.6898 | 0.7445 | 0.7161 | 0.9118 | |
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| 0.0027 | 6.0 | 1512 | 0.9481 | 0.6780 | 0.7550 | 0.7145 | 0.9120 | |
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| 0.0019 | 7.0 | 1764 | 0.9328 | 0.6917 | 0.7513 | 0.7203 | 0.9150 | |
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| 0.0008 | 8.0 | 2016 | 0.9570 | 0.6976 | 0.7520 | 0.7238 | 0.9143 | |
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| 0.0005 | 9.0 | 2268 | 0.9586 | 0.7016 | 0.7518 | 0.7258 | 0.9154 | |
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| 0.0003 | 10.0 | 2520 | 0.9565 | 0.6945 | 0.7520 | 0.7221 | 0.9151 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.0 |
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