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--- |
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license: apache-2.0 |
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library_name: peft |
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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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base_model: mistralai/Mistral-7B-v0.1 |
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model-index: |
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- name: billm-mistral-7b-conll03-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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# billm-mistral-7b-conll03-ner |
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This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2046 |
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- Precision: 0.9273 |
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- Recall: 0.9393 |
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- F1: 0.9333 |
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- Accuracy: 0.9864 |
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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: 0.0002 |
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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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| 0.0499 | 1.0 | 1756 | 0.1085 | 0.9196 | 0.9287 | 0.9241 | 0.9845 | |
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| 0.0233 | 2.0 | 3512 | 0.0997 | 0.9249 | 0.9226 | 0.9237 | 0.9845 | |
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| 0.0097 | 3.0 | 5268 | 0.1343 | 0.9292 | 0.9386 | 0.9339 | 0.9870 | |
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| 0.0036 | 4.0 | 7024 | 0.1651 | 0.9245 | 0.9386 | 0.9315 | 0.9864 | |
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| 0.0012 | 5.0 | 8780 | 0.1839 | 0.9257 | 0.9373 | 0.9315 | 0.9863 | |
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| 0.0005 | 6.0 | 10536 | 0.2027 | 0.9258 | 0.9386 | 0.9321 | 0.9864 | |
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| 0.0002 | 7.0 | 12292 | 0.2022 | 0.9276 | 0.9384 | 0.9330 | 0.9864 | |
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| 0.0002 | 8.0 | 14048 | 0.2040 | 0.9274 | 0.9388 | 0.9331 | 0.9864 | |
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| 0.0001 | 9.0 | 15804 | 0.2048 | 0.9270 | 0.9393 | 0.9331 | 0.9864 | |
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| 0.0001 | 10.0 | 17560 | 0.2046 | 0.9273 | 0.9393 | 0.9333 | 0.9864 | |
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### Framework versions |
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- PEFT 0.9.0 |
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- Transformers 4.38.2 |
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- Pytorch 2.0.1 |
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- Datasets 2.16.0 |
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- Tokenizers 0.15.0 |
