Zero-Shot Classification
Transformers
PyTorch
Safetensors
French
English
bloom
text-classification
text-generation-inference
Instructions to use cmarkea/bloomz-3b-nli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cmarkea/bloomz-3b-nli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="cmarkea/bloomz-3b-nli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("cmarkea/bloomz-3b-nli") model = AutoModelForSequenceClassification.from_pretrained("cmarkea/bloomz-3b-nli") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1db6b64fb5ade55c18efbd8fb5fd7f58c14e073514cc409cd908a25b1bc12b64
- Size of remote file:
- 6.01 GB
- SHA256:
- 04707c4f60f1a890e20e9c7d19c7c547d5073879b9bce89a1fb8644efb22b0ca
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.