初始化项目，由ModelHub XC社区提供模型

Model: cyberagent/xlm-roberta-large-jnli-jsick
Source: Original Platform

README.md

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+---
+language: ja
+license: cc-by-4.0
+library_name: sentence-transformers
+tags:
+- xlm-roberta
+- nli
+datasets:
+- jnli
+- jsick
+---
+
+# Japanese Natural Language Inference Model
+This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class, [gradient accumulation PR](https://github.com/UKPLab/sentence-transformers/pull/1092), and the code from [CyberAgentAILab/japanese-nli-model](https://github.com/CyberAgentAILab/japanese-nli-model).
+
+## Training Data
+The model was trained on the [JGLUE-JNLI](https://github.com/yahoojapan/JGLUE) and [JSICK](https://github.com/verypluming/JSICK) datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral.
+
+## Usage
+```python
+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)
+```