update readme

README.md

@@ -3,14 +3,21 @@ license: cc-by-sa-4.0
 language: ja
 pipeline_tag: zero-shot-classification
 tags:
-- tohoku-nlp/bert-base-japanese-v3
+  - cross-encoder
+  - tohoku-nlp/bert-base-japanese-v3
+  - nli
+  - natural-language-inference
 datasets:
 - shunk031/jsnli
 library_name: sentence-transformers
 ---
 
 
-# Cross-Encoder for Natural Language Inference
+# Cross-Encoder for Natural Language Inference(NLI) for Japanese
+
+> [!NOTE]
+> Considering the results of the JNLI evaluation result, we recommend using [akiFQC/bert-base-japanese-v3_nli-jsnli-jnli-jsick](https://huggingface.co/akiFQC/bert-base-japanese-v3_nli-jsnli-jnli-jsick) for natural language inference in Japanese.
+
 This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class. 
 This model is based on [tohoku-nlp/bert-base-japanese-v3](https://huggingface.co/tohoku-nlp/bert-base-japanese-v3).
 
@@ -61,4 +68,8 @@ sent = "Appleは先程、iPhoneの最新機種について発表しました。"
 candidate_labels = ["技術", "スポーツ", "政治"]
 res = classifier(sent, candidate_labels)
 print(res)
-``` 
+```
+
+## Benchmarks
+
+JGLUE-JNLI validation set accuracy: 0.770