Text Classification
Transformers
PyTorch
TensorBoard
Safetensors
English
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use JeremiahZ/bert-base-uncased-sst2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JeremiahZ/bert-base-uncased-sst2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="JeremiahZ/bert-base-uncased-sst2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("JeremiahZ/bert-base-uncased-sst2") model = AutoModelForSequenceClassification.from_pretrained("JeremiahZ/bert-base-uncased-sst2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1d917214bb0f5aec6da0ad5650d8be5092e5b95d3e832474ea6902dbb2777cc8
- Size of remote file:
- 438 MB
- SHA256:
- 3025d135ba69468cfde54be8c24366c97867fc942b7c65df8fa152116f8568dd
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