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:
- 8474f29b455a0e06d82f7c66651d6e5338defb6c6c9f1ecc8c387f84028b577a
- Size of remote file:
- 258 kB
- SHA256:
- d81ef2c36236065dfa1bb7e2e452fff7269820fc849ea4ef581f9852598c923a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.