Instructions to use prajjwal1/albert-base-v2-mnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prajjwal1/albert-base-v2-mnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="prajjwal1/albert-base-v2-mnli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("prajjwal1/albert-base-v2-mnli") model = AutoModelForSequenceClassification.from_pretrained("prajjwal1/albert-base-v2-mnli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from prajjwal1/albert-base-v2-mnli: direct link, hf CLI and curl.
- Browser
- Download file 986 Bytes
-
https://huggingface.co/prajjwal1/albert-base-v2-mnli/resolve/0847a47993bb5aaa32e53d8437e7996ad474b7fb/training_args.bin
- Command line
-
hf download hf://prajjwal1/albert-base-v2-mnli@0847a47993bb5aaa32e53d8437e7996ad474b7fb/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/prajjwal1/albert-base-v2-mnli/resolve/0847a47993bb5aaa32e53d8437e7996ad474b7fb/training_args.bin
986 Bytes
- Xet hash:
- bdfdc4a5fb7d56c0c45eb24f874a38ae2ad5fb56d6eb94eeafca990fe8cf7eed
- Size of remote file:
- 986 Bytes
- SHA256:
- 07940779830bdc247498c6f983cd46980867b14a9fd96cc8314639d90c442748
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.