Text Classification
Transformers
PyTorch
TensorBoard
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use responsibility-framing/predict-perception-xlmr-cause-none 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-none 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-none")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("responsibility-framing/predict-perception-xlmr-cause-none") model = AutoModelForSequenceClassification.from_pretrained("responsibility-framing/predict-perception-xlmr-cause-none", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 49b9aa320dc1510498baf2de016dbe7a15465b26f2af6649f0c78232fbf4098f
- Size of remote file:
- 3.12 kB
- SHA256:
- 72861f7a67164e7c5bf2742de83dae6601905ae6f54be1ca25d6570e92400c48
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.