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:
- 6ac8754a3ebab7a05306624ce98bbb268988a42d2c72598b7a36ae907f8b5703
- Size of remote file:
- 2.93 kB
- SHA256:
- 5f1b73c185074660152d1ba4d174d61805c587dc85c819c510cb2943ccd7fe71
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