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")# pip install -U transformers accelerate # 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
Download pytorch_model.bin from lschlessinger/bert-finetuned-math-prob-classification: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/lschlessinger/bert-finetuned-math-prob-classification/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://lschlessinger/bert-finetuned-math-prob-classification/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/lschlessinger/bert-finetuned-math-prob-classification/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 3f3e682d211ed2ff5c72e6daedbe06c9732c128ea502e792932b40944d0edd8c
- Size of remote file:
- 438 MB
- SHA256:
- e77122837e62241ce38c4516cb96f750a9e0523072b1d2e8caac13e79f7df900
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