Instructions to use EricPeter/sw-text-classification-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EricPeter/sw-text-classification-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="EricPeter/sw-text-classification-model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("EricPeter/sw-text-classification-model") model = AutoModelForSequenceClassification.from_pretrained("EricPeter/sw-text-classification-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6e913ec584f55985c2408c569a3e11f2a3bfa0a6d6b6f9fc11c2405ae88d0aeb
- Size of remote file:
- 711 MB
- SHA256:
- b45453108ffd73b35b557fd4abd003d0ef68b8f5745ac1bf7eafbc898b740aca
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.