Instructions to use amyma21/sincere_question_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use amyma21/sincere_question_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="amyma21/sincere_question_classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("amyma21/sincere_question_classification") model = AutoModelForSequenceClassification.from_pretrained("amyma21/sincere_question_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from amyma21/sincere_question_classification: direct link, hf CLI and curl.
- Browser
- Download file 369 Bytes
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https://huggingface.co/amyma21/sincere_question_classification/resolve/refs%2Fpr%2F2/README.md
- Command line
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hf download hf://amyma21/sincere_question_classification@refs/pr/2/README.md
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curl -L -o README.md https://huggingface.co/amyma21/sincere_question_classification/resolve/refs%2Fpr%2F2/README.md
369 Bytes
| license: apache-2.0 | |
| datasets: | |
| - microsoft/rStar-Coder | |
| - FreedomIntelligence/medical-o1-reasoning-SFT | |
| - NousResearch/Hermes-3-Dataset | |
| - MegaScience/MegaScience | |
| language: | |
| - hi | |
| - en | |
| - sa | |
| - ru | |
| - ur | |
| - ta | |
| - te | |
| - kn | |
| - mr | |
| metrics: | |
| - accuracy | |
| - brier_score | |
| - character | |
| - code_eval | |
| base_model: | |
| - openai/gpt-oss-20b | |
| pipeline_tag: reinforcement-learning | |
| library_name: flair | |