Token Classification
Transformers
Safetensors
qwen2
Generated from Trainer
prm
trl
math
process-reward-model
qwen2.5
sharp
text-generation-inference
Instructions to use ZaandaTeika/Qwen2.5-Math-7B-Instruct-SHARP-Math-Span-Classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ZaandaTeika/Qwen2.5-Math-7B-Instruct-SHARP-Math-Span-Classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="ZaandaTeika/Qwen2.5-Math-7B-Instruct-SHARP-Math-Span-Classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("ZaandaTeika/Qwen2.5-Math-7B-Instruct-SHARP-Math-Span-Classifier") model = AutoModelForTokenClassification.from_pretrained("ZaandaTeika/Qwen2.5-Math-7B-Instruct-SHARP-Math-Span-Classifier") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4bd2f092eeec244c0448f13e31edd4b25e625568713e4155993a3dea90c7437a
- Size of remote file:
- 11.4 MB
- SHA256:
- 9c5ae00e602b8860cbd784ba82a8aa14e8feecec692e7076590d014d7b7fdafa
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