Text Classification
Transformers
PyTorch
TensorBoard
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use responsibility-framing/predict-perception-xlmr-focus-concept with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use responsibility-framing/predict-perception-xlmr-focus-concept with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="responsibility-framing/predict-perception-xlmr-focus-concept")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("responsibility-framing/predict-perception-xlmr-focus-concept") model = AutoModelForSequenceClassification.from_pretrained("responsibility-framing/predict-perception-xlmr-focus-concept", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bd506b1a7c2ba2e24cf5bd8ce768e97fbde6a4daf207d951336547e1c958fab7
- Size of remote file:
- 1.11 GB
- SHA256:
- 9f72fe5d20ca9675d0253fc6d7b2a29609b40bbeed275e80e6165d98f9441d51
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