Instructions to use hd10/semeval2020_task11_tc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hd10/semeval2020_task11_tc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hd10/semeval2020_task11_tc")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hd10/semeval2020_task11_tc") model = AutoModelForSequenceClassification.from_pretrained("hd10/semeval2020_task11_tc") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 790e44cd0532a4cf72d8f1a9745bce06a37c0becb135739fb38b411308f64fe0
- Size of remote file:
- 1.63 GB
- SHA256:
- 3cd415a20d6492a8888fc72a6797eaa822d0708772b0d86ea89f24a02db1d33c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.