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license: apache-2.0 |
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base_model: distilbert/distilroberta-base |
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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: distilroberta-base-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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# distilroberta-base-finetuned-ner-harem |
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This model is a fine-tuned version of [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2169 |
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- Precision: 0.6576 |
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- Recall: 0.6851 |
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- F1: 0.6711 |
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- Accuracy: 0.9489 |
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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 | 0.2950 | 0.4796 | 0.4388 | 0.4583 | 0.9183 | |
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| 0.3687 | 2.0 | 564 | 0.2216 | 0.5693 | 0.5821 | 0.5756 | 0.9362 | |
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| 0.3687 | 3.0 | 846 | 0.2170 | 0.5850 | 0.6060 | 0.5953 | 0.9373 | |
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| 0.1701 | 4.0 | 1128 | 0.1990 | 0.6352 | 0.6522 | 0.6436 | 0.9464 | |
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| 0.1701 | 5.0 | 1410 | 0.1978 | 0.6558 | 0.6910 | 0.6730 | 0.9481 | |
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| 0.1123 | 6.0 | 1692 | 0.1998 | 0.6378 | 0.6701 | 0.6536 | 0.9495 | |
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| 0.1123 | 7.0 | 1974 | 0.2112 | 0.6643 | 0.6851 | 0.6745 | 0.9490 | |
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| 0.0809 | 8.0 | 2256 | 0.2153 | 0.6571 | 0.6806 | 0.6686 | 0.9480 | |
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| 0.0572 | 9.0 | 2538 | 0.2133 | 0.6647 | 0.6836 | 0.6740 | 0.9502 | |
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| 0.0572 | 10.0 | 2820 | 0.2169 | 0.6576 | 0.6851 | 0.6711 | 0.9489 | |
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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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