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:
- 39395d4389748aa77770827314ac605b1968ff3bc6f520a4fb7c1434a1018e6d
- Size of remote file:
- 3.12 kB
- SHA256:
- c278fa4752744cd32696a3b68d3fadacb9fa2df11c15662d3756f95d398371cf
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