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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: 0.1198 |
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- Precision: 0.8118 |
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- Recall: 0.8560 |
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- F1: 0.8333 |
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- Accuracy: 0.9732 |
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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 | 276 | 0.2493 | 0.5422 | 0.4216 | 0.4744 | 0.9417 | |
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| 0.3195 | 2.0 | 552 | 0.1788 | 0.7273 | 0.7019 | 0.7143 | 0.9602 | |
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| 0.3195 | 3.0 | 828 | 0.1485 | 0.7550 | 0.7428 | 0.7488 | 0.9633 | |
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| 0.1376 | 4.0 | 1104 | 0.1542 | 0.7092 | 0.7956 | 0.7499 | 0.9619 | |
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| 0.1376 | 5.0 | 1380 | 0.1326 | 0.7449 | 0.8135 | 0.7777 | 0.9658 | |
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| 0.0887 | 6.0 | 1656 | 0.1152 | 0.8228 | 0.8305 | 0.8266 | 0.9728 | |
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| 0.0887 | 7.0 | 1932 | 0.1223 | 0.7721 | 0.8424 | 0.8057 | 0.9692 | |
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| 0.0639 | 8.0 | 2208 | 0.1184 | 0.7852 | 0.8501 | 0.8164 | 0.9721 | |
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| 0.0639 | 9.0 | 2484 | 0.1184 | 0.8252 | 0.8484 | 0.8366 | 0.9734 | |
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| 0.0505 | 10.0 | 2760 | 0.1198 | 0.8118 | 0.8560 | 0.8333 | 0.9732 | |
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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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