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--- |
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license: mit |
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base_model: FacebookAI/roberta-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: roberta_crf_ner_finetuned |
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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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# roberta_crf_ner_finetuned |
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This model is a fine-tuned version of [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: nan |
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- Precision: 0.7893 |
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- Recall: 0.6294 |
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- F1: 0.6950 |
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- Accuracy: 0.8037 |
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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: 8 |
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- eval_batch_size: 8 |
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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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- lr_scheduler_warmup_steps: 500 |
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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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| 0.0 | 1.0 | 85 | nan | 1.0 | 0.0 | 0.0 | 0.7707 | |
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| 0.0 | 2.0 | 170 | nan | 0.5437 | 0.1932 | 0.1694 | 0.8848 | |
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| 0.0 | 3.0 | 255 | nan | 0.4412 | 0.3360 | 0.3230 | 0.9228 | |
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| 0.0 | 4.0 | 340 | nan | 0.4888 | 0.6412 | 0.5523 | 0.9161 | |
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| 0.0 | 5.0 | 425 | nan | 0.6312 | 0.6266 | 0.6206 | 0.9451 | |
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| 0.0 | 6.0 | 510 | nan | 0.6319 | 0.6851 | 0.6560 | 0.9484 | |
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| 0.0 | 7.0 | 595 | nan | 0.6655 | 0.7110 | 0.6869 | 0.9518 | |
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| 0.0 | 8.0 | 680 | nan | 0.6341 | 0.7094 | 0.6693 | 0.9508 | |
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| 0.0 | 9.0 | 765 | nan | 0.6745 | 0.7127 | 0.6924 | 0.9533 | |
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| 0.0 | 10.0 | 850 | nan | 0.6886 | 0.7175 | 0.7019 | 0.9548 | |
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### Framework versions |
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- Transformers 4.44.0 |
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- Pytorch 2.4.0 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
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