Instructions to use hf-internal-testing/tiny-random-MT5ForTokenClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-MT5ForTokenClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="hf-internal-testing/tiny-random-MT5ForTokenClassification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-MT5ForTokenClassification") model = AutoModelForTokenClassification.from_pretrained("hf-internal-testing/tiny-random-MT5ForTokenClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from hf-internal-testing/tiny-random-MT5ForTokenClassification: direct link, hf CLI and curl.
- Browser
- Download file 32.1 MB
-
https://huggingface.co/hf-internal-testing/tiny-random-MT5ForTokenClassification/resolve/refs%2Fpr%2F39/model.safetensors
- Command line
-
hf download hf://hf-internal-testing/tiny-random-MT5ForTokenClassification@refs/pr/39/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/hf-internal-testing/tiny-random-MT5ForTokenClassification/resolve/refs%2Fpr%2F39/model.safetensors
32.1 MB
- Xet hash:
- 6e33c2cab28ad8bef32908f8eeb34522fda833ea76e20dfbda40ef00ff99bbaf
- Size of remote file:
- 32.1 MB
- SHA256:
- 07ad4aa849c500df55dc83acf009916760f4ffde82770ff5bf5393359afeb0e2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.