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
metadata
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