Instructions to use hf-internal-testing/tiny-random-UMT5ForTokenClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-UMT5ForTokenClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="hf-internal-testing/tiny-random-UMT5ForTokenClassification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-UMT5ForTokenClassification") model = AutoModelForTokenClassification.from_pretrained("hf-internal-testing/tiny-random-UMT5ForTokenClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from hf-internal-testing/tiny-random-UMT5ForTokenClassification: direct link, hf CLI and curl.
- Browser
- Download file 258 kB
-
https://huggingface.co/hf-internal-testing/tiny-random-UMT5ForTokenClassification/resolve/refs%2Fpr%2F46/model.safetensors
- Command line
-
hf download hf://hf-internal-testing/tiny-random-UMT5ForTokenClassification@refs/pr/46/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/hf-internal-testing/tiny-random-UMT5ForTokenClassification/resolve/refs%2Fpr%2F46/model.safetensors
258 kB
- Xet hash:
- ec35e56ef6309be45a2dfa4469504f4dfd7e4968169ca4d1da1c6b5eb3bf8c1e
- Size of remote file:
- 258 kB
- SHA256:
- 393eed07a6f0df68c34dc4cc78a50e77b425c90f602f4addc024f48113d479f9
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