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README.md

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 ### Model Description
 
-This model is a fine-tuned version of the `cl-tohoku/bert-base-japanese-v3`, specifically optimized for Named Entity Recognition (NER) tasks. 
+This model is a fine-tuned version of the `tohoku-nlp/bert-base-japanese-v3`, specifically optimized for Named Entity Recognition (NER) tasks. 
 It is fine-tuned using a Japanese named entity extraction dataset derived from Wikipedia, which was developed and made publicly available by Stockmark Inc. ([NER Wikipedia Dataset](https://github.com/stockmarkteam/ner-wikipedia-dataset)).
 
 ### Intended Use
@@ -99,13 +99,7 @@ The model has been evaluated on various entity types to assess its precision, re
   - **F1 Score:** 0.8125
   - **Sample Count:** 140
 
-
-### Summary
-
-These results demonstrate the model's capability to perform reliably across different types of entities, making it highly effective for comprehensive Named Entity Recognition tasks within the Japanese language. 
-The robust accuracy and F1 scores across various categories show the model's balanced performance, catering to both common and less frequent entity types found in Japanese text.
-
-### Limitations and Biases
-
-The performance of the model may vary based on the text's domain, and the accuracy might not be uniformly high across different types of named entities.
-Users should also be aware of potential biases inherent in the training dataset, which was solely built from Wikipedia articles and might not adequately represent less formal or diverse language usage.
+### Note 
+You might not able to use the model with huggingface Inference API. 
+The intended use for the model is given in the following repository: [KeshavSingh29/fa_ner_japanese](https://github.com/KeshavSingh29/fa_ner_japanese)
+If you have any questions, please feel free to contact me or raise an issue at the above repo.