Instructions to use antonthieme/esm2_t6_8M_UR50D_56621481 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use antonthieme/esm2_t6_8M_UR50D_56621481 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="antonthieme/esm2_t6_8M_UR50D_56621481")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("antonthieme/esm2_t6_8M_UR50D_56621481") model = AutoModelForSequenceClassification.from_pretrained("antonthieme/esm2_t6_8M_UR50D_56621481", device_map="auto") - Notebooks
- Google Colab
- Kaggle
esm2_t6_8M_UR50D_56621481
This model is a fine-tuned version of facebook/esm2_t6_8M_UR50D on the None dataset.
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: 1
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 147 | 0.5300 | 0.7667 |
Framework versions
- Transformers 4.46.3
- Pytorch 2.5.1.post103
- Datasets 3.1.0
- Tokenizers 0.20.4
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Model tree for antonthieme/esm2_t6_8M_UR50D_56621481
Base model
facebook/esm2_t6_8M_UR50D