Instructions to use kxia1729/esm-finetuned-proj1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kxia1729/esm-finetuned-proj1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="kxia1729/esm-finetuned-proj1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("kxia1729/esm-finetuned-proj1") model = AutoModelForTokenClassification.from_pretrained("kxia1729/esm-finetuned-proj1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
esm-finetuned-proj1
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: 1
- eval_batch_size: 1
- 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: 5
Training results
Framework versions
- Transformers 4.51.1
- Pytorch 2.6.0
- Datasets 3.5.0
- Tokenizers 0.21.1
- Downloads last month
- 23
Model tree for kxia1729/esm-finetuned-proj1
Base model
facebook/esm2_t6_8M_UR50D