Instructions to use thinkingface/deid_roberta_i2b2_q with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use thinkingface/deid_roberta_i2b2_q with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="thinkingface/deid_roberta_i2b2_q")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("thinkingface/deid_roberta_i2b2_q") model = AutoModelForTokenClassification.from_pretrained("thinkingface/deid_roberta_i2b2_q", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.onnx from thinkingface/deid_roberta_i2b2_q: direct link, hf CLI and curl.
- Browser
- Download file 1.42 GB
-
https://huggingface.co/thinkingface/deid_roberta_i2b2_q/resolve/main/model.onnx
- Command line
-
hf download hf://thinkingface/deid_roberta_i2b2_q/model.onnx
-
curl -L -o model.onnx https://huggingface.co/thinkingface/deid_roberta_i2b2_q/resolve/main/model.onnx
1.42 GB
- Xet hash:
- 0be6741b5bc4e9c74e1a7c2be2ab2abbc7252e07184ffb8cd0a26158e1636474
- Size of remote file:
- 1.42 GB
- SHA256:
- 1c62d741500457a84a15d7b06e673cdbece4c7c80eeff08949fbaf2c2160a9f3
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