Instructions to use cloudqi/cqi_classification_pt_v0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cloudqi/cqi_classification_pt_v0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="cloudqi/cqi_classification_pt_v0")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("cloudqi/cqi_classification_pt_v0") model = AutoModelForSequenceClassification.from_pretrained("cloudqi/cqi_classification_pt_v0") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d07c57c4c228938d47fa00aaae0dd276ed48c5f75a821a9c4e4474cf1abf26ad
- Size of remote file:
- 540 MB
- SHA256:
- cbb2a82fac7350eaa0baf9aa30fb9ebc9884cb9d6c48746ae6034e89771311dc
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.