Instructions to use TheJournal/high_accuracy_Bert_Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TheJournal/high_accuracy_Bert_Model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="TheJournal/high_accuracy_Bert_Model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("TheJournal/high_accuracy_Bert_Model") model = AutoModelForSequenceClassification.from_pretrained("TheJournal/high_accuracy_Bert_Model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from TheJournal/high_accuracy_Bert_Model: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/TheJournal/high_accuracy_Bert_Model/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://TheJournal/high_accuracy_Bert_Model/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/TheJournal/high_accuracy_Bert_Model/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- b43cecfb790e256b14efa88545d12944db0f82a915095f892267c8001ca78d1d
- Size of remote file:
- 438 MB
- SHA256:
- 8b9da9d74cc133925333b13fdf2fb23a5f7177928fec3139977b61614152d670
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