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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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base_model: mistralai/Mistral-7B-v0.1 |
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datasets: |
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- conll2003 |
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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: billm-mistral-7b-conll03-ner-maxlen-256 |
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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-maxlen-256 |
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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 the conll2003 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2232 |
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- Precision: 0.9277 |
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- Recall: 0.9363 |
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- F1: 0.9320 |
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- Accuracy: 0.9863 |
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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: 0.0001 |
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- train_batch_size: 2 |
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- eval_batch_size: 2 |
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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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| 0.0329 | 1.0 | 7021 | 0.1599 | 0.9256 | 0.9357 | 0.9306 | 0.9856 | |
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| 0.0145 | 2.0 | 14042 | 0.1789 | 0.9312 | 0.9340 | 0.9326 | 0.9860 | |
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| 0.0106 | 3.0 | 21063 | 0.1931 | 0.9288 | 0.9359 | 0.9324 | 0.9864 | |
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| 0.0065 | 4.0 | 28084 | 0.2161 | 0.9277 | 0.9361 | 0.9319 | 0.9863 | |
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| 0.0043 | 5.0 | 35105 | 0.2168 | 0.9276 | 0.9363 | 0.9319 | 0.9864 | |
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| 0.002 | 6.0 | 42126 | 0.2250 | 0.9274 | 0.9359 | 0.9316 | 0.9863 | |
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| 0.0027 | 7.0 | 49147 | 0.2246 | 0.9269 | 0.9356 | 0.9312 | 0.9862 | |
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| 0.0023 | 8.0 | 56168 | 0.2235 | 0.9277 | 0.9364 | 0.9321 | 0.9863 | |
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| 0.0024 | 9.0 | 63189 | 0.2232 | 0.9276 | 0.9364 | 0.9320 | 0.9863 | |
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| 0.0016 | 10.0 | 70210 | 0.2232 | 0.9277 | 0.9363 | 0.9320 | 0.9863 | |
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### Framework versions |
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- PEFT 0.10.0 |
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- Transformers 4.41.2 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
