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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download special_tokens_map.json from responsibility-framing/predict-perception-xlmr-cause-object: direct link, hf CLI and curl.
- Browser
- Download file 239 Bytes
-
https://huggingface.co/responsibility-framing/predict-perception-xlmr-cause-object/resolve/main/special_tokens_map.json
- Command line
-
hf download hf://responsibility-framing/predict-perception-xlmr-cause-object/special_tokens_map.json
-
curl -L -o special_tokens_map.json https://huggingface.co/responsibility-framing/predict-perception-xlmr-cause-object/resolve/main/special_tokens_map.json
239 Bytes
| {"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "sep_token": "</s>", "pad_token": "<pad>", "cls_token": "<s>", "mask_token": {"content": "<mask>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": false}} |