Text Classification
Transformers
PyTorch
TensorBoard
Safetensors
English
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use JeremiahZ/bert-base-uncased-mnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JeremiahZ/bert-base-uncased-mnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="JeremiahZ/bert-base-uncased-mnli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("JeremiahZ/bert-base-uncased-mnli") model = AutoModelForSequenceClassification.from_pretrained("JeremiahZ/bert-base-uncased-mnli") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9243b8db0264d5e9f25173a0f1bbeea885924c21f899cc9650ed3962bc46fd6b
- Size of remote file:
- 3.31 kB
- SHA256:
- 492f959edcdc3689dc62e55f099dc8b4408152d0ff36100afe208ced8b8e1d94
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