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--- |
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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: NousResearch/Llama-2-7b-hf |
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model-index: |
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- name: billm-llama-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-llama-7b-conll03-ner |
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This model is a fine-tuned version of [NousResearch/Llama-2-7b-hf](https://huggingface.co/NousResearch/Llama-2-7b-hf) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1783 |
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- Precision: 0.9150 |
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- Recall: 0.9330 |
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- F1: 0.9239 |
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- Accuracy: 0.9851 |
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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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| 0.0477 | 1.0 | 1756 | 0.0998 | 0.9116 | 0.9283 | 0.9199 | 0.9842 | |
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| 0.0201 | 2.0 | 3512 | 0.0986 | 0.9152 | 0.9251 | 0.9201 | 0.9842 | |
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| 0.0089 | 3.0 | 5268 | 0.1195 | 0.9128 | 0.9278 | 0.9202 | 0.9843 | |
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| 0.0025 | 4.0 | 7024 | 0.1564 | 0.9129 | 0.9341 | 0.9234 | 0.9851 | |
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| 0.0013 | 5.0 | 8780 | 0.1669 | 0.9140 | 0.9316 | 0.9227 | 0.9850 | |
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| 0.0006 | 6.0 | 10536 | 0.1736 | 0.9155 | 0.9328 | 0.9241 | 0.9852 | |
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| 0.0003 | 7.0 | 12292 | 0.1755 | 0.9144 | 0.9325 | 0.9233 | 0.9851 | |
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| 0.0003 | 8.0 | 14048 | 0.1782 | 0.9145 | 0.9328 | 0.9236 | 0.9851 | |
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| 0.0003 | 9.0 | 15804 | 0.1782 | 0.9144 | 0.9326 | 0.9234 | 0.9851 | |
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| 0.0002 | 10.0 | 17560 | 0.1783 | 0.9150 | 0.9330 | 0.9239 | 0.9851 | |
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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 |
