Instructions to use hf-internal-testing/tiny-random-RobertaForTokenClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-RobertaForTokenClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="hf-internal-testing/tiny-random-RobertaForTokenClassification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-RobertaForTokenClassification") model = AutoModelForTokenClassification.from_pretrained("hf-internal-testing/tiny-random-RobertaForTokenClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from hf-internal-testing/tiny-random-RobertaForTokenClassification: direct link, hf CLI and curl.
- Browser
- Download file 349 kB
-
https://huggingface.co/hf-internal-testing/tiny-random-RobertaForTokenClassification/resolve/refs%2Fpr%2F3/model.safetensors
- Command line
-
hf download hf://hf-internal-testing/tiny-random-RobertaForTokenClassification@refs/pr/3/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/hf-internal-testing/tiny-random-RobertaForTokenClassification/resolve/refs%2Fpr%2F3/model.safetensors
349 kB
- Xet hash:
- 8bcdd13ff132898886227982eeadda4b41f749e9448370cfb45c3b2d2715bf3b
- Size of remote file:
- 349 kB
- SHA256:
- 43aa1199b7b09e83b2d14630bf7497ba662d01db4a7f9eed778dfaf5d9c52dca
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.