Instructions to use blackbird/bert-base-uncased-MNLI-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use blackbird/bert-base-uncased-MNLI-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="blackbird/bert-base-uncased-MNLI-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("blackbird/bert-base-uncased-MNLI-v1") model = AutoModelForSequenceClassification.from_pretrained("blackbird/bert-base-uncased-MNLI-v1") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7dda4406c96b9ae9306cd62b28c1e0774522c8f87636e216bd8477239692d130
- Size of remote file:
- 438 MB
- SHA256:
- d459c2f0372f0dc64a51c991fe92a96b74b93871f0fce51d4bc1381ed56212c6
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