Instructions to use hf-tiny-model-private/tiny-random-EsmForTokenClassification 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-EsmForTokenClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="hf-tiny-model-private/tiny-random-EsmForTokenClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-EsmForTokenClassification") model = AutoModelForTokenClassification.from_pretrained("hf-tiny-model-private/tiny-random-EsmForTokenClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 98828d303fdd9189115dcc9d0713c5a7b10f7303553833b773030f83312daf72
- Size of remote file:
- 334 kB
- SHA256:
- 8921fc83dc63a5c2005a07a22170c4d9a864fe781f7e8d9af4604a7554ddbb44
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