# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("qunfengd/esm2_t12_35M_UR50D-finetuned-AMP_Classification_AntiGramPositive")
model = AutoModelForSequenceClassification.from_pretrained("qunfengd/esm2_t12_35M_UR50D-finetuned-AMP_Classification_AntiGramPositive", device_map="auto")Quick Links
esm2_t12_35M_UR50D-finetuned-AMP_Classification_AntiGramPositive
This model is a fine-tuned version of facebook/esm2_t12_35M_UR50D on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: 0.3822
- Train Accuracy: 0.8324
- Validation Loss: 0.4433
- Validation Accuracy: 0.8020
- Epoch: 2
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:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.0}
- training_precision: float32
Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|---|---|---|---|---|
| 0.5531 | 0.7238 | 0.4958 | 0.7804 | 0 |
| 0.4654 | 0.7885 | 0.4547 | 0.7921 | 1 |
| 0.3822 | 0.8324 | 0.4433 | 0.8020 | 2 |
Framework versions
- Transformers 4.40.2
- TensorFlow 2.15.0
- Datasets 2.19.1
- Tokenizers 0.19.1
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Model tree for qunfengd/esm2_t12_35M_UR50D-finetuned-AMP_Classification_AntiGramPositive
Base model
facebook/esm2_t12_35M_UR50D
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="qunfengd/esm2_t12_35M_UR50D-finetuned-AMP_Classification_AntiGramPositive")