deberta-v3-large-clause-metaphor

from transformers import AutoTokenizer, AutoModelForTokenClassification
import torch

model_path = "your-org/deberta-v3-large-clause-metaphor"  # or local path
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForTokenClassification.from_pretrained(model_path)

words = ["The", "government", "attacked", "the", "proposal", "."]
inputs = tokenizer(
    [words],
    is_split_into_words=True,
    return_tensors="pt",
    truncation=True,
    max_length=192,
)
word_ids = inputs.word_ids(batch_index=0)
with torch.no_grad():
    logits = model(**inputs).logits
preds = logits.argmax(dim=-1)[0].tolist()

# Map subwords back to words (first subword per word)
word_predictions = {}
for i, wid in enumerate(word_ids):
    if wid is not None and wid not in word_predictions:
        word_predictions[wid] = 1 if preds[i] == 1 else 0
for i, w in enumerate(words):
    label = "metaphor" if word_predictions.get(i, 0) == 1 else "non_metaphor"
    print(f"{w}\t{label}")