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:
- 865d42b0da6494212cfcb38cf4fa2e667710dde39da281442337d6004fb2c303
- Size of remote file:
- 438 MB
- SHA256:
- 46ab4c69ac181b7a7fc42e8a5cf1e2c4e54c79e61900d3f0453e52ccd57424a2
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