Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use responsibility-framing/predict-perception-bert-cause-none 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-none 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-none")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("responsibility-framing/predict-perception-bert-cause-none") model = AutoModelForSequenceClassification.from_pretrained("responsibility-framing/predict-perception-bert-cause-none") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bf90a026771eb56048d4fe66bfecafa895103f5d76e0024c1b8f32546fd947bc
- Size of remote file:
- 443 MB
- SHA256:
- 630c876d1da65f292b48063efa1e34dcd5418b1f9e326ebbc3fdbdbe2d492b13
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