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")# pip install -U transformers accelerate # 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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from hf-internal-testing/tiny-random-T5ForTokenClassification: direct link, hf CLI and curl.
- Browser
- Download file 222 kB
-
https://huggingface.co/hf-internal-testing/tiny-random-T5ForTokenClassification/resolve/refs%2Fpr%2F5/model.safetensors
- Command line
-
hf download hf://hf-internal-testing/tiny-random-T5ForTokenClassification@refs/pr/5/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/hf-internal-testing/tiny-random-T5ForTokenClassification/resolve/refs%2Fpr%2F5/model.safetensors
222 kB
- Xet hash:
- efe79f014ecaf989d4a4455b136249aaeda703910d9b89289b3b09f41aa3dd3d
- Size of remote file:
- 222 kB
- SHA256:
- 1b790f37ec3b39f0318b823258ccee908c62a66a84f0bc6d75b0ea68ba771d1c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.