Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use responsibility-framing/predict-perception-bert-cause-human 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-human 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-human")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("responsibility-framing/predict-perception-bert-cause-human") model = AutoModelForSequenceClassification.from_pretrained("responsibility-framing/predict-perception-bert-cause-human") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1919be80a49b1638e4665de6395d32d1d2314565679c1a1fa11983548663ef54
- Size of remote file:
- 443 MB
- SHA256:
- 72f6240525b56b6a64e04ae6d9ddff397c2af228b880291e6b637dbf0e55b55d
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