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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: 0.2053 |
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- Precision: 0.6638 |
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- Recall: 0.6836 |
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- F1: 0.6735 |
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- Accuracy: 0.9498 |
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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 | 0.2811 | 0.4809 | 0.4896 | 0.4852 | 0.9235 | |
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| 0.3394 | 2.0 | 564 | 0.2218 | 0.5679 | 0.5866 | 0.5771 | 0.9377 | |
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| 0.3394 | 3.0 | 846 | 0.2205 | 0.5708 | 0.5776 | 0.5742 | 0.9347 | |
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| 0.1635 | 4.0 | 1128 | 0.2027 | 0.6290 | 0.6478 | 0.6382 | 0.9469 | |
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| 0.1635 | 5.0 | 1410 | 0.1895 | 0.6542 | 0.6806 | 0.6672 | 0.9504 | |
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| 0.106 | 6.0 | 1692 | 0.2055 | 0.6334 | 0.6448 | 0.6391 | 0.9470 | |
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| 0.106 | 7.0 | 1974 | 0.1992 | 0.6328 | 0.6687 | 0.6502 | 0.9502 | |
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| 0.0744 | 8.0 | 2256 | 0.2051 | 0.6804 | 0.6925 | 0.6864 | 0.9513 | |
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| 0.0522 | 9.0 | 2538 | 0.1998 | 0.6745 | 0.6866 | 0.6805 | 0.9502 | |
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| 0.0522 | 10.0 | 2820 | 0.2053 | 0.6638 | 0.6836 | 0.6735 | 0.9498 | |
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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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