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:
- 99119a5a23b702b3fe564cfa54b98559a53d8e517d142960021f9f7b6735c552
- Size of remote file:
- 2.99 kB
- SHA256:
- 75d45a0058bf26f1d59f8aaad091dcfc9869a132b1b866171da10162ccfc6452
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