Instructions to use hf-internal-testing/tiny-random-EsmForTokenClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-EsmForTokenClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="hf-internal-testing/tiny-random-EsmForTokenClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-EsmForTokenClassification") model = AutoModelForTokenClassification.from_pretrained("hf-internal-testing/tiny-random-EsmForTokenClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c0efbd31da845ed043d552a265ad7a2d1a38ba7939d821359072d1607aabffb3
- Size of remote file:
- 309 kB
- SHA256:
- 2939a875b80274fac6a0cc2b90d048b52fae7f3e617dfb3a7e84cf8e3faedb9e
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