Instructions to use hf-internal-testing/tiny-random-FunnelForTokenClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-FunnelForTokenClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="hf-internal-testing/tiny-random-FunnelForTokenClassification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-FunnelForTokenClassification") model = AutoModelForTokenClassification.from_pretrained("hf-internal-testing/tiny-random-FunnelForTokenClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from hf-internal-testing/tiny-random-FunnelForTokenClassification: direct link, hf CLI and curl.
- Browser
- Download file 315 kB
-
https://huggingface.co/hf-internal-testing/tiny-random-FunnelForTokenClassification/resolve/refs%2Fpr%2F1/model.safetensors
- Command line
-
hf download hf://hf-internal-testing/tiny-random-FunnelForTokenClassification@refs/pr/1/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/hf-internal-testing/tiny-random-FunnelForTokenClassification/resolve/refs%2Fpr%2F1/model.safetensors
315 kB
- Xet hash:
- b865f6e29d6942bc9e8e54e61bb764c936e52e879853225953f40ac84c3a42df
- Size of remote file:
- 315 kB
- SHA256:
- ce85844b0d5d39e4149ab9c0d781c0ab8b280faee9f7eb0f7c5d19fc0ea79122
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.