Instructions to use asifraza/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use asifraza/results with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="asifraza/results")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("asifraza/results") model = AutoModelForSequenceClassification.from_pretrained("asifraza/results", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from asifraza/results: direct link, hf CLI and curl.
- Browser
- Download file 433 MB
-
https://huggingface.co/asifraza/results/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://asifraza/results/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/asifraza/results/resolve/main/pytorch_model.bin
433 MB
- Xet hash:
- da1f9406f4b6d9316cc9a5f45564ab026007befff397db6c38b4a8cd700d6c86
- Size of remote file:
- 433 MB
- SHA256:
- ccfb68f1b16b863ec159ad5166455a96822596ba15a923c145dcd651f0b91578
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.