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_quantized.onnx from thinkingface/deid_roberta_i2b2_q: direct link, hf CLI and curl.
- Browser
- Download file 356 MB
-
https://huggingface.co/thinkingface/deid_roberta_i2b2_q/resolve/main/model_quantized.onnx
- Command line
-
hf download hf://thinkingface/deid_roberta_i2b2_q/model_quantized.onnx
-
curl -L -o model_quantized.onnx https://huggingface.co/thinkingface/deid_roberta_i2b2_q/resolve/main/model_quantized.onnx
356 MB
- Xet hash:
- 902bf6c70dc13762b58aeeb8dfa45b8bfe450bf1a91e3e348b0edda38d74f616
- Size of remote file:
- 356 MB
- SHA256:
- 010383b74fb6d6181784c2dd963ce348698ba97d1c11b77dae935df3920abbe1
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