Text Classification
Transformers
PyTorch
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use lschlessinger/bert-finetuned-math-prob-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lschlessinger/bert-finetuned-math-prob-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="lschlessinger/bert-finetuned-math-prob-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("lschlessinger/bert-finetuned-math-prob-classification") model = AutoModelForSequenceClassification.from_pretrained("lschlessinger/bert-finetuned-math-prob-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Librarian Bot: Update Hugging Face dataset ID
#3
by librarian-bot - opened
README.md
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@@ -3,7 +3,7 @@ license: apache-2.0
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tags:
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- generated_from_trainer
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datasets:
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- competition_math
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widget:
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- text: Find the number of positive divisors of 9!.
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example_title: Number theory
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tags:
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- generated_from_trainer
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datasets:
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- hendrycks/competition_math
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widget:
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- text: Find the number of positive divisors of 9!.
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example_title: Number theory
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