Instructions to use hf-internal-testing/tiny-random-T5ForTokenClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-T5ForTokenClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="hf-internal-testing/tiny-random-T5ForTokenClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-T5ForTokenClassification") model = AutoModelForTokenClassification.from_pretrained("hf-internal-testing/tiny-random-T5ForTokenClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2d061ef6655d45a6e86af4f0368a85402f8e0d0579f909ecf3f881af54c6ee8f
- Size of remote file:
- 222 kB
- SHA256:
- d7bee8b8b4f80f5f87234163294d20bddd2631e7e45240fcc766bc2e0b44c273
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.