Instructions to use BrianS15/prot_bert-finetuned-tchard with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BrianS15/prot_bert-finetuned-tchard with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="BrianS15/prot_bert-finetuned-tchard")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("BrianS15/prot_bert-finetuned-tchard") model = AutoModelForMaskedLM.from_pretrained("BrianS15/prot_bert-finetuned-tchard") - Notebooks
- Google Colab
- Kaggle
prot_bert-finetuned-tchard
This model is a fine-tuned version of Rostlab/prot_bert on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0390
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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 1.0 | 800 | 0.0454 |
| 0.0667 | 2.0 | 1600 | 0.0376 |
| 0.0394 | 3.0 | 2400 | 0.0390 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.0
- Tokenizers 0.13.3
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Model tree for BrianS15/prot_bert-finetuned-tchard
Base model
Rostlab/prot_bert