Text Classification
Transformers
PyTorch
TensorBoard
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use responsibility-framing/predict-perception-xlmr-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-xlmr-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-xlmr-cause-object")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("responsibility-framing/predict-perception-xlmr-cause-object") model = AutoModelForSequenceClassification.from_pretrained("responsibility-framing/predict-perception-xlmr-cause-object") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d566f625c5698675ceab20f628bc02dc2d7c817161704159b7f3290e65a335e3
- Size of remote file:
- 3.12 kB
- SHA256:
- 7a55fcc16c45e34218fb549753362631537cc8908abde5b25ac72b600538ea7f
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