Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use responsibility-framing/predict-perception-bert-cause-concept 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-concept 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-concept")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("responsibility-framing/predict-perception-bert-cause-concept") model = AutoModelForSequenceClassification.from_pretrained("responsibility-framing/predict-perception-bert-cause-concept", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 429e1dcd6d70b7b4a475b21509529294f672160a7c596212a144753187cb7f17
- Size of remote file:
- 443 MB
- SHA256:
- abb27f987762a1c858a61985e9e0fa56ae0859d5a2030e7d254b6a5e39c24f87
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