Instructions to use dima806/disaster-tweet-distilbert-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dima806/disaster-tweet-distilbert-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dima806/disaster-tweet-distilbert-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dima806/disaster-tweet-distilbert-classification") model = AutoModelForSequenceClassification.from_pretrained("dima806/disaster-tweet-distilbert-classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1a86dc493daa0d99afe7807342c7f05de34ddfae3c2e89d1b95339a9fee65d53
- Size of remote file:
- 3.9 kB
- SHA256:
- b7c01d8e107b4a20c0ba0f3692dae4e25d8f1dffe1d23d6e4f4bdf92b87ab5ea
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.