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roberta-base-bne-finetuned-fact
filevich/roberta-base-bne-finetuned-fact
roberta-base-bne-finetuned-fact is a token classification model from filevich. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Token Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- roberta
- Access
- Public
- Created
- Aug 27, 2023
- Updated
- Aug 27, 2023
- SHA
- 403ec01c
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Base models
Datasets
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
roberta-base-bne-finetuned-fact
This model is a fine-tuned version of PlanTL-GOB-ES/roberta-base-bne on the fact2020 dataset. It achieves the following results on the evaluation set:
- Loss: 0.0368
- Precision: 0.9953
- Recall: 0.9906
- F1: 0.9911
- Accuracy: 0.9906
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 116 | 0.0404 | 0.9945 | 0.9899 | 0.9901 | 0.9899 |
| No log | 2.0 | 232 | 0.0359 | 0.9948 | 0.9903 | 0.9906 | 0.9903 |
| No log | 3.0 | 348 | 0.0368 | 0.9953 | 0.9906 | 0.9911 | 0.9906 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3
- Task
- Token Classification
- Library
- transformers
- Type
- roberta
- License
- apache-2.0
- Languages
- es
- Created
- Aug 27, 2023
- Updated
- Aug 27, 2023