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")# 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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9c122dd5e85e57d11e3dc41010e8f726037d7befe1418b334cdd0d18f384bf61
- Size of remote file:
- 258 kB
- SHA256:
- 54a2b071b5fbfc470aa842757147de5e630e89ce015ada0aba53962a482acf01
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.