Instructions to use vadams2327/ESM_EndToEnd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vadams2327/ESM_EndToEnd with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="vadams2327/ESM_EndToEnd")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("vadams2327/ESM_EndToEnd") model = AutoModelForSequenceClassification.from_pretrained("vadams2327/ESM_EndToEnd", device_map="auto") - Notebooks
- Google Colab
- Kaggle
ESM_EndToEnd
This model is a fine-tuned version of facebook/esm2_t33_650M_UR50D on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.9955
- Accuracy: 0.6857
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: 3
- eval_batch_size: 3
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 35 | 1.0016 | 0.5714 |
| No log | 2.0 | 70 | 0.7018 | 0.6 |
| No log | 3.0 | 105 | 0.6536 | 0.6286 |
| No log | 4.0 | 140 | 1.1304 | 0.6571 |
| No log | 5.0 | 175 | 0.9955 | 0.6857 |
Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1
- Datasets 2.20.0
- Tokenizers 0.19.1
- Downloads last month
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Model tree for vadams2327/ESM_EndToEnd
Base model
facebook/esm2_t33_650M_UR50D