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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 72f2ffe16d024e9b149e785cfc233ff572b0484d824e123ac084f2abbae8c435
- Size of remote file:
- 340 kB
- SHA256:
- 52e6dfea6cab1f1f52ffe934760b88c1353c96105f2e08753589ee134aae7243
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