Text Classification
Transformers
PyTorch
TensorBoard
English
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use DunnBC22/roberta-base-Tweet_About_Disaster_Or_Not with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DunnBC22/roberta-base-Tweet_About_Disaster_Or_Not with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="DunnBC22/roberta-base-Tweet_About_Disaster_Or_Not")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("DunnBC22/roberta-base-Tweet_About_Disaster_Or_Not") model = AutoModelForSequenceClassification.from_pretrained("DunnBC22/roberta-base-Tweet_About_Disaster_Or_Not") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5bacc649e5a242b3d9ed93f682f5d1e4fb249c9a4ecf50118285164c8c396192
- Size of remote file:
- 3.52 kB
- SHA256:
- d84c2a301c6d2081cde1663b0cc0b7dbc1d544acf630bb40959306ed7fb3eb67
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.