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xlm-roberta-base-finetuned-detests-wandb
Pablo94/xlm-roberta-base-finetuned-detests-wandb
xlm-roberta-base-finetuned-detests-wandb is a text classification model from Pablo94. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- mit
- Model type
- xlm-roberta
- Access
- Public
- Created
- Jan 16, 2024
- Updated
- Jan 16, 2024
- SHA
- cc29d307
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Base models
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. -->
xlm-roberta-base-finetuned-detests-wandb
This model is a fine-tuned version of xlm-roberta-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.4658
- Accuracy: 0.8232
- F1-score: 0.7604
- Precision: 0.7501
- Recall: 0.7739
- Auc: 0.7739
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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1-score | Precision | Recall | Auc |
|---|---|---|---|---|---|---|---|---|
| 0.4464 | 1.0 | 174 | 0.4631 | 0.8118 | 0.7509 | 0.7377 | 0.7716 | 0.7716 |
| 0.2623 | 2.0 | 348 | 0.4658 | 0.8232 | 0.7604 | 0.7501 | 0.7739 | 0.7739 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
- Task
- Text Classification
- Library
- transformers
- Type
- xlm-roberta
- License
- mit
- Created
- Jan 16, 2024
- Updated
- Jan 16, 2024