Instructions to use hadifar/eventclassifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hadifar/eventclassifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hadifar/eventclassifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hadifar/eventclassifier") model = AutoModelForSequenceClassification.from_pretrained("hadifar/eventclassifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bcdfedb2705c820a3f5b09b230f6c62c679b649e4754c8011e0804be383db5ed
- Size of remote file:
- 499 MB
- SHA256:
- 7a4b45a1daa1502e2507e34251d4daf3bd290c152ad37d6ca4799fc0241f4ff2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.