Instructions to use cublya/bge-m3-zeroshot-v2.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cublya/bge-m3-zeroshot-v2.0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="cublya/bge-m3-zeroshot-v2.0")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("cublya/bge-m3-zeroshot-v2.0") model = AutoModelForSequenceClassification.from_pretrained("cublya/bge-m3-zeroshot-v2.0", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from cublya/bge-m3-zeroshot-v2.0: direct link, hf CLI and curl.
- Browser
- Download file 4.92 kB
-
https://huggingface.co/cublya/bge-m3-zeroshot-v2.0/resolve/main/training_args.bin
- Command line
-
hf download hf://cublya/bge-m3-zeroshot-v2.0/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/cublya/bge-m3-zeroshot-v2.0/resolve/main/training_args.bin
4.92 kB
- Xet hash:
- b2c8225d15cb94d903615f544ef88cf01c08f0e19ec44ddf18fb858c4910235e
- Size of remote file:
- 4.92 kB
- SHA256:
- bb8e9eb20e1fc002f3b83afe07f8ae31e102ae6f3e32bf2112a5e35507e96299
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