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--- |
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base_model: microsoft/mdeberta-v3-base |
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library_name: peft |
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license: mit |
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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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tags: |
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- generated_from_trainer |
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model-index: |
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- name: mdeberta-v3-base-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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# mdeberta-v3-base-finetuned-ner |
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This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1094 |
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- Precision: 0.9101 |
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- Recall: 0.9567 |
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- F1: 0.9328 |
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- Accuracy: 0.9757 |
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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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| 0.2289 | 1.0 | 1167 | 0.1496 | 0.8186 | 0.8964 | 0.8557 | 0.9524 | |
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| 0.1466 | 2.0 | 2334 | 0.1193 | 0.8771 | 0.9312 | 0.9033 | 0.9677 | |
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| 0.1064 | 3.0 | 3501 | 0.1143 | 0.8768 | 0.9451 | 0.9097 | 0.9673 | |
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| 0.0808 | 4.0 | 4668 | 0.0978 | 0.8968 | 0.9491 | 0.9222 | 0.9724 | |
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| 0.0668 | 5.0 | 5835 | 0.1111 | 0.8889 | 0.9533 | 0.9200 | 0.9713 | |
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| 0.0559 | 6.0 | 7002 | 0.1168 | 0.9054 | 0.9561 | 0.9301 | 0.9746 | |
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| 0.0459 | 7.0 | 8169 | 0.1085 | 0.9174 | 0.9457 | 0.9313 | 0.9749 | |
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| 0.0421 | 8.0 | 9336 | 0.1077 | 0.9141 | 0.9516 | 0.9325 | 0.9759 | |
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| 0.0324 | 9.0 | 10503 | 0.1084 | 0.9148 | 0.9575 | 0.9357 | 0.9766 | |
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| 0.03 | 10.0 | 11670 | 0.1094 | 0.9101 | 0.9567 | 0.9328 | 0.9757 | |
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### Framework versions |
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- PEFT 0.11.1 |
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- Transformers 4.42.3 |
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- Pytorch 2.1.2 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
