Instructions to use karths/binary_classification_train_reli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use karths/binary_classification_train_reli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="karths/binary_classification_train_reli")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("karths/binary_classification_train_reli") model = AutoModelForSequenceClassification.from_pretrained("karths/binary_classification_train_reli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from karths/binary_classification_train_reli: direct link, hf CLI and curl.
- Browser
- Download file 328 MB
-
https://huggingface.co/karths/binary_classification_train_reli/resolve/main/model.safetensors
- Command line
-
hf download hf://karths/binary_classification_train_reli/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/karths/binary_classification_train_reli/resolve/main/model.safetensors
328 MB
- Xet hash:
- 63943df41fe0876b6b9a61cb62cf67d16b3dd47524b8b488288ae62c889eee63
- Size of remote file:
- 328 MB
- SHA256:
- b5d5ab69550e5ac79424dd51c00e0698d24a72dbba6c1abb3277f2657d120dd3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.