Token Classification
Transformers
PyTorch
xlm-roberta
linguistics
cognition
methaphors
information-extraction
classification
cognitive-linguistics
conceptual-metaphors
Instructions to use lwachowiak/Metaphor-Detection-XLMR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lwachowiak/Metaphor-Detection-XLMR with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="lwachowiak/Metaphor-Detection-XLMR")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("lwachowiak/Metaphor-Detection-XLMR") model = AutoModelForTokenClassification.from_pretrained("lwachowiak/Metaphor-Detection-XLMR", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3ef94981aba671e4681801cb8af2a710519525b4070111cb6cfcbbc0eb736d8b
- Size of remote file:
- 1.11 GB
- SHA256:
- 07e63745e3e0eaebac31d7e88a8b22ee4e985f1fd5b1cc7cfe525e34ffac2b28
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