Instructions to use hf-tiny-model-private/tiny-random-EsmForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-EsmForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-tiny-model-private/tiny-random-EsmForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-EsmForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-tiny-model-private/tiny-random-EsmForSequenceClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 55248a95e0e65abb697aa9dadd2bb80b129f31a59ad0f584705951af0f89be56
- Size of remote file:
- 224 kB
- SHA256:
- 349ac5136f45d0f0d6c0675296a112a521dd429703714cebffc97233fe1ba09d
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