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:
- ca0b0162d5b93ff0b0398a0045bd641ddbeb43ec13b513ff10ed5c524f254df9
- Size of remote file:
- 3.12 kB
- SHA256:
- 0d7aac79437613b4cfeb26f8672b96e1becd48ac7b8cd300d970aae865c7311b
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