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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: 0.2254 |
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- Precision: 0.6447 |
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- Recall: 0.6716 |
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- F1: 0.6579 |
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- Accuracy: 0.9475 |
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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 | 0.2896 | 0.4849 | 0.4567 | 0.4704 | 0.9207 | |
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| 0.3471 | 2.0 | 564 | 0.2266 | 0.5677 | 0.5821 | 0.5748 | 0.9348 | |
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| 0.3471 | 3.0 | 846 | 0.2240 | 0.5925 | 0.6164 | 0.6042 | 0.9377 | |
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| 0.1655 | 4.0 | 1128 | 0.2015 | 0.6455 | 0.6522 | 0.6488 | 0.9478 | |
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| 0.1655 | 5.0 | 1410 | 0.2017 | 0.6431 | 0.6776 | 0.6599 | 0.9485 | |
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| 0.1072 | 6.0 | 1692 | 0.2095 | 0.6164 | 0.6522 | 0.6338 | 0.9455 | |
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| 0.1072 | 7.0 | 1974 | 0.2086 | 0.6556 | 0.6791 | 0.6672 | 0.9495 | |
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| 0.0758 | 8.0 | 2256 | 0.2278 | 0.6322 | 0.6672 | 0.6492 | 0.9466 | |
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| 0.0529 | 9.0 | 2538 | 0.2226 | 0.6429 | 0.6716 | 0.6569 | 0.9480 | |
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| 0.0529 | 10.0 | 2820 | 0.2254 | 0.6447 | 0.6716 | 0.6579 | 0.9475 | |
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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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