Instructions to use LsTam/MQ-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LsTam/MQ-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="LsTam/MQ-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("LsTam/MQ-classification") model = AutoModelForSequenceClassification.from_pretrained("LsTam/MQ-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 65e6e48b1508902d8054e8b1fa2d39b8f6e8424f97597f376d2d7554e0bc97da
- Size of remote file:
- 1.11 GB
- SHA256:
- fc5d0670076901b272eaecb09476ffd2df24902d15e34042d03c7fec64b9f3d4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.