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:
- 2cb7313853bdd4f440697d09f5d20172db1a6b7a567116437eb2495059f24384
- Size of remote file:
- 3.12 kB
- SHA256:
- d43a34ebfb512e0b04c871fcc046858ea9e4ff115fec535c792c7742f76c7f5d
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