Instructions to use everyl12/stance_class_mod with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use everyl12/stance_class_mod with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="everyl12/stance_class_mod")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("everyl12/stance_class_mod") model = AutoModelForSequenceClassification.from_pretrained("everyl12/stance_class_mod", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 34463916ddc8fa2fe11052377ae3698e6e24b04d9d9e3a27ac884909379a28d0
- Size of remote file:
- 3.39 kB
- SHA256:
- 8f428e845c4efd23479247e1d08788731cdec9cddd847d1302b2a654d76d48f4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.