Instructions to use hf-tiny-model-private/tiny-random-GPT2ForTokenClassification 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-GPT2ForTokenClassification 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-GPT2ForTokenClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-GPT2ForTokenClassification") model = AutoModelForTokenClassification.from_pretrained("hf-tiny-model-private/tiny-random-GPT2ForTokenClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d333da6b6f1dafea79e689eaa18d6924ed3331c0da0ae4316aa88d63e917a7c5
- Size of remote file:
- 1.67 MB
- SHA256:
- bc96f8f9fc24b16e9f6c3cc7ca81b21afcf2da1b8afd796f85105f17cfd03e70
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