Text Classification
Transformers
PyTorch
TensorBoard
Safetensors
English
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use JeremiahZ/bert-base-uncased-rte with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JeremiahZ/bert-base-uncased-rte with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="JeremiahZ/bert-base-uncased-rte")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("JeremiahZ/bert-base-uncased-rte") model = AutoModelForSequenceClassification.from_pretrained("JeremiahZ/bert-base-uncased-rte") - Notebooks
- Google Colab
- Kaggle
Commit ·
cb5f2f1
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Parent(s): dc76949
Librarian Bot: Add base_model information to model (#4)
Browse files- Librarian Bot: Add base_model information to model (9e72e64da703d881e5ea040cc5019227f4007ac8)
Co-authored-by: Librarian Bot (Bot) <librarian-bot@users.noreply.huggingface.co>
README.md
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- glue
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metrics:
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- accuracy
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model-index:
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- name: bert-base-uncased-rte
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results:
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- glue
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metrics:
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- accuracy
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base_model: bert-base-uncased
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model-index:
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- name: bert-base-uncased-rte
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results:
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