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esm2_t30_150M_UR50D-finetuned-localization
chengkunvv/esm2_t30_150M_UR50D-finetuned-localization
esm2_t30_150M_UR50D-finetuned-localization is a text classification model from chengkunvv. 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
- Jul 15, 2024
- Updated
- Jul 18, 2024
- SHA
- 5195d916
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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_t30_150M_UR50D-finetuned-localization
This model is a fine-tuned version of facebook/esm2_t30_150M_UR50D on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.3995
- Accuracy: 0.8309
- Ppv: 0.8144
- Npv: 0.8496
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: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Ppv | Npv |
|---|---|---|---|---|---|---|
| No log | 1.0 | 205 | 0.4242 | 0.8016 | 0.8147 | 0.7893 |
| No log | 2.0 | 410 | 0.4036 | 0.8186 | 0.8087 | 0.8294 |
| 0.4233 | 3.0 | 615 | 0.3912 | 0.8296 | 0.8116 | 0.8504 |
| 0.4233 | 4.0 | 820 | 0.3995 | 0.8309 | 0.8144 | 0.8496 |
Framework versions
- Transformers 4.42.3
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
- Task
- Text Classification
- Library
- transformers
- Type
- esm
- License
- mit
- Created
- Jul 15, 2024
- Updated
- Jul 18, 2024