Instructions to use Maikobi/scibert_error_detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Maikobi/scibert_error_detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Maikobi/scibert_error_detection")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Maikobi/scibert_error_detection") model = AutoModelForSequenceClassification.from_pretrained("Maikobi/scibert_error_detection", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from Maikobi/scibert_error_detection: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/Maikobi/scibert_error_detection/resolve/main/model.safetensors
- Command line
-
hf download hf://Maikobi/scibert_error_detection/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/Maikobi/scibert_error_detection/resolve/main/model.safetensors
438 MB
- Xet hash:
- 963ccd9b9dadf1874aecaa997ff3051ceefff6241724b77c23cc287577300ae6
- Size of remote file:
- 438 MB
- SHA256:
- 4f771223e21f92e6f82e6bf77d7ecbe664def462bf08c0dafa78caae44edb2e3
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