The model was trained on the
JGLUE-JNLI
and
JSICK
datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral.
Usage
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained('cyberagent/xlm-roberta-large-jnli-jsick')
model = AutoModelForSequenceClassification.from_pretrained('cyberagent/xlm-roberta-large-jnli-jsick')
features = tokenizer(["子供が走っている猫を見ている", "猫が走っている"], ["猫が走っている", "子供が走っている"], padding=True, truncation=True, return_tensors="pt")
model.eval()
with torch.no_grad():
scores = model(**features).logits
label_mapping = ['contradiction', 'entailment', 'neutral']
labels = [label_mapping[score_max] for score_max in scores.argmax(dim=1)]
print(labels)
Runs of cyberagent xlm-roberta-large-jnli-jsick on huggingface.co
293
Total runs
-6
24-hour runs
2
3-day runs
-2
7-day runs
103
30-day runs
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