Instructions to use howey/bert-base-uncased-mnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use howey/bert-base-uncased-mnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="howey/bert-base-uncased-mnli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("howey/bert-base-uncased-mnli") model = AutoModelForSequenceClassification.from_pretrained("howey/bert-base-uncased-mnli") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ea3477dcc642f9625f42239a50b0ab321344b086fb4a9280b2f3e9f47a2169de
- Size of remote file:
- 438 MB
- SHA256:
- b4cd88c75d26e17d328c061dba1b2c518e51d32d0fac4e15aed603454d2f5cf8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.