Instructions to use vargr/yt-title-grader-quantized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vargr/yt-title-grader-quantized with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="vargr/yt-title-grader-quantized")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("vargr/yt-title-grader-quantized") model = AutoModelForSequenceClassification.from_pretrained("vargr/yt-title-grader-quantized", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
Upload tokenizer.json with huggingface_hub
d85117c -
Upload vocab.txt with huggingface_hub
15b418d -
Upload special_tokens_map.json with huggingface_hub
e09de44 -
Upload tokenizer_config.json with huggingface_hub
e0c30fb -
Upload config.json with huggingface_hub
747c8ff -
Upload ort_config.json with huggingface_hub
12cbb68 -
Upload model_optimized_quantized.onnx with huggingface_hub
c7a5513 -
initial commit
807fc4a