Instructions to use avuhong/ESM1b_AAV2_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use avuhong/ESM1b_AAV2_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="avuhong/ESM1b_AAV2_classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("avuhong/ESM1b_AAV2_classification") model = AutoModelForSequenceClassification.from_pretrained("avuhong/ESM1b_AAV2_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6f801091c29e6ddb58a434fb9d2659689838a6496309f333870367f9de2d16de
- Size of remote file:
- 2.61 GB
- SHA256:
- 439a56625af685860eadf90637106a6f1cd9c1cc78ea057130a7fdf9b041b07c
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