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--- |
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license: apache-2.0 |
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base_model: distilbert/distilbert-base-uncased |
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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: distilbert-base-uncased-finetuned-ner-harem |
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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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# distilbert-base-uncased-finetuned-ner-harem |
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This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2794 |
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- Precision: 0.6556 |
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- Recall: 0.6324 |
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- F1: 0.6438 |
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- Accuracy: 0.9448 |
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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: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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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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| No log | 1.0 | 282 | 0.3860 | 0.3575 | 0.2411 | 0.2880 | 0.9035 | |
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| 0.4189 | 2.0 | 564 | 0.3048 | 0.5051 | 0.4165 | 0.4566 | 0.9227 | |
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| 0.4189 | 3.0 | 846 | 0.2893 | 0.5924 | 0.5025 | 0.5438 | 0.9303 | |
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| 0.209 | 4.0 | 1128 | 0.2752 | 0.5640 | 0.5649 | 0.5644 | 0.9335 | |
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| 0.209 | 5.0 | 1410 | 0.2880 | 0.6466 | 0.5616 | 0.6011 | 0.9409 | |
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| 0.1252 | 6.0 | 1692 | 0.2656 | 0.6404 | 0.5885 | 0.6134 | 0.9426 | |
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| 0.1252 | 7.0 | 1974 | 0.2662 | 0.6367 | 0.6324 | 0.6345 | 0.9419 | |
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| 0.0859 | 8.0 | 2256 | 0.2717 | 0.6584 | 0.6273 | 0.6425 | 0.9444 | |
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| 0.0593 | 9.0 | 2538 | 0.2774 | 0.6590 | 0.6290 | 0.6437 | 0.9440 | |
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| 0.0593 | 10.0 | 2820 | 0.2794 | 0.6556 | 0.6324 | 0.6438 | 0.9448 | |
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### Framework versions |
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- Transformers 4.41.1 |
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- Pytorch 2.1.2 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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