Instructions to use edithram23/Redaction_personal_info with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use edithram23/Redaction_personal_info with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="edithram23/Redaction_personal_info")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("edithram23/Redaction_personal_info") model = AutoModelForSequenceClassification.from_pretrained("edithram23/Redaction_personal_info", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from edithram23/Redaction_personal_info: direct link, hf CLI and curl.
- Browser
- Download file 1.78 GB
-
https://huggingface.co/edithram23/Redaction_personal_info/resolve/main/optimizer.pt
- Command line
-
hf download hf://edithram23/Redaction_personal_info/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/edithram23/Redaction_personal_info/resolve/main/optimizer.pt
1.78 GB
- Xet hash:
- 971561b57d7e70c9ff7eabc3b900184585a5a828a02c77850c4f05583c11c529
- Size of remote file:
- 1.78 GB
- SHA256:
- dabb61dda448eedbf5836bd58b09f23e78d5cce12b461637526f72db2ac82519
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