Instructions to use dpravinv/fold_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dpravinv/fold_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dpravinv/fold_2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dpravinv/fold_2") model = AutoModelForSequenceClassification.from_pretrained("dpravinv/fold_2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from dpravinv/fold_2: direct link, hf CLI and curl.
- Browser
- Download file 5.43 kB
-
https://huggingface.co/dpravinv/fold_2/resolve/main/training_args.bin
- Command line
-
hf download hf://dpravinv/fold_2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/dpravinv/fold_2/resolve/main/training_args.bin
5.43 kB
- Xet hash:
- 24f847032344383c97b634955249358b929cd8dd5c83256ce70fcba626656cfb
- Size of remote file:
- 5.43 kB
- SHA256:
- 2970cf00dba7b437cb55dca603400159f95b665d280d2361cc91762036839264
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.