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--- |
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license: mit |
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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: cv-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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# cv-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 an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0956 |
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- Precision: 0.8906 |
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- Recall: 0.9325 |
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- F1: 0.9111 |
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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: 5e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 4 |
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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: 10.0 |
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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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| No log | 1.0 | 91 | 0.2049 | 0.6618 | 0.7362 | 0.6970 | 0.9534 | |
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| 0.5036 | 2.0 | 182 | 0.1156 | 0.7873 | 0.8630 | 0.8234 | 0.9722 | |
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| 0.1442 | 3.0 | 273 | 0.1078 | 0.8262 | 0.9039 | 0.8633 | 0.9771 | |
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| 0.0757 | 4.0 | 364 | 0.1179 | 0.8652 | 0.9059 | 0.8851 | 0.9780 | |
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| 0.0526 | 5.0 | 455 | 0.0907 | 0.888 | 0.9080 | 0.8979 | 0.9837 | |
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| 0.0342 | 6.0 | 546 | 0.0972 | 0.8926 | 0.9346 | 0.9131 | 0.9832 | |
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| 0.0245 | 7.0 | 637 | 0.1064 | 0.8937 | 0.9284 | 0.9107 | 0.9834 | |
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| 0.0188 | 8.0 | 728 | 0.0965 | 0.8980 | 0.9366 | 0.9169 | 0.9850 | |
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| 0.0159 | 9.0 | 819 | 0.0999 | 0.91 | 0.9305 | 0.9201 | 0.9846 | |
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| 0.0141 | 10.0 | 910 | 0.0956 | 0.8906 | 0.9325 | 0.9111 | 0.9851 | |
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### Framework versions |
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- Transformers 4.24.0.dev0 |
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- Pytorch 1.12.1+cu113 |
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- Datasets 2.6.1 |
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- Tokenizers 0.13.1 |
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