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%2F8/model.safetensors
- Command line
-
hf download hf://hf-internal-testing/tiny-random-MT5ForTokenClassification@refs/pr/8/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/hf-internal-testing/tiny-random-MT5ForTokenClassification/resolve/refs%2Fpr%2F8/model.safetensors
32.1 MB
- Xet hash:
- 81284408965526bac0f1fc84dc1e3d472d6aefa355817f714342426ac1eb0e81
- Size of remote file:
- 32.1 MB
- SHA256:
- a2bc688b02affe25e602696f3143cc030ba2675e611c41817e0a9a660183f8d9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.