Instructions to use ranjman/esm2_t6_8M_UR50D-finetuned-localization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ranjman/esm2_t6_8M_UR50D-finetuned-localization with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ranjman/esm2_t6_8M_UR50D-finetuned-localization")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ranjman/esm2_t6_8M_UR50D-finetuned-localization") model = AutoModelForSequenceClassification.from_pretrained("ranjman/esm2_t6_8M_UR50D-finetuned-localization", device_map="auto") - Notebooks
- Google Colab
- Kaggle
esm2_t6_8M_UR50D-finetuned-localization
This model is a fine-tuned version of facebook/esm2_t6_8M_UR50D on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.5149
- Accuracy: 0.7883
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 | Accuracy |
|---|---|---|---|---|
| 0.5377 | 1.0 | 1238 | 0.5221 | 0.7959 |
| 0.5064 | 2.0 | 2476 | 0.5234 | 0.7741 |
| 0.4695 | 3.0 | 3714 | 0.5149 | 0.7883 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1
- Datasets 2.10.0
- Tokenizers 0.13.2
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