Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use responsibility-framing/predict-perception-bert-cause-object with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use responsibility-framing/predict-perception-bert-cause-object with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="responsibility-framing/predict-perception-bert-cause-object")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("responsibility-framing/predict-perception-bert-cause-object") model = AutoModelForSequenceClassification.from_pretrained("responsibility-framing/predict-perception-bert-cause-object") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5282832243453d7802ea62dd46391a18a24f25e7dd7c9bbc65fca0bcb83948e7
- Size of remote file:
- 443 MB
- SHA256:
- a0e0325b55f4b10f9a3311442a2b4eb287edaf613aad8d9ec7086211a11d09d3
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