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esm2_t12_35M_UR50D-finetuned-localization
antonthieme/esm2_t12_35M_UR50D-finetuned-localization
esm2_t12_35M_UR50D-finetuned-localization is a text classification model from antonthieme. 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
- esm
- Access
- Public
- Created
- Nov 27, 2024
- Updated
- Dec 5, 2024
- SHA
- a3a6f0ac
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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. -->
esm2_t12_35M_UR50D-finetuned-localization
This model is a fine-tuned version of facebook/esm2_t12_35M_UR50D on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.5978
- Accuracy: 0.8
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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 147 | 0.4621 | 0.7846 |
| No log | 2.0 | 294 | 0.4769 | 0.7974 |
| No log | 3.0 | 441 | 0.5287 | 0.8 |
| 0.3616 | 4.0 | 588 | 0.5449 | 0.8051 |
| 0.3616 | 5.0 | 735 | 0.5978 | 0.8 |
Framework versions
- Transformers 4.46.3
- Pytorch 2.5.1.post103
- Datasets 3.1.0
- Tokenizers 0.20.4
- Task
- Text Classification
- Library
- transformers
- Type
- esm
- License
- mit
- Created
- Nov 27, 2024
- Updated
- Dec 5, 2024