Instructions to use hadifar/entityextraction with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hadifar/entityextraction with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="hadifar/entityextraction")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("hadifar/entityextraction") model = AutoModelForTokenClassification.from_pretrained("hadifar/entityextraction", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
Adding `safetensors` variant of this model
This is an automated PR created with https://huggingface.co/spaces/safetensors/convert This new file is equivalent to `pytorch_model.bin` but safe in the sense that no arbitrary code can be put into it. These files also happen to load much faster than their pytorch counterpart: https://colab.research.google.com/github/huggingface/notebooks/blob/main/safetensors_doc/en/speed.ipynb The widgets on your model page will run using this model even if this is not merged making sure the file actually works. If you find any issues: please report here: https://huggingface.co/spaces/safetensors/convert/discussions Feel free to ignore this PR.63d6cfe
-
Upload tokenizer
3804ea1 -
Upload RobertaForTokenClassification
becda2e
-
Upload tokenizer
382b139 -
Upload RobertaForTokenClassification
b1a852b
-
Upload tokenizer
29ae375 -
Upload RobertaForTokenClassification
2ac3f0b -
initial commit
00fcdc0