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") - Notebooks
- Google Colab
- Kaggle
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# bert-finetuned-math-prob-classification
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the competition_math dataset. Specifically, it was trained as a multi-class multi-label model on the problem text. The problem types (labels) used here are "Counting & Probability", "Prealgebra", "Algebra", "Number Theory", "Geometry", "Intermediate Algebra", and "Precalculus".
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## Model description
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# bert-finetuned-math-prob-classification
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the part of the [competition_math dataset](https://huggingface.co/datasets/competition_math). Specifically, it was trained as a multi-class multi-label model on the problem text. The problem types (labels) used here are "Counting & Probability", "Prealgebra", "Algebra", "Number Theory", "Geometry", "Intermediate Algebra", and "Precalculus".
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## Model description
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