Update README.md

README.md

@@ -6,39 +6,24 @@ tags:
 - cantonese
 - fill-mask
 license: other
-library_name: bart-base-jax
-co2_eq_emissions:
-  emissions: 6.29
-  source: estimated by using ML CO2 Calculator
-  training_type: second-stage pre-training
-  hardware_used: Google Cloud TPU v4-16
+
 ---
 
 # bart-base-cantonese
 
-This is the Cantonese model of BART base. It is obtained by a second-stage pre-training on the [LIHKG dataset](https://github.com/ayaka14732/lihkg-scraper) based on the [fnlp/bart-base-chinese](https://huggingface.co/fnlp/bart-base-chinese) model.
-
-This project is supported by Cloud TPUs from Google's [TPU Research Cloud](https://sites.research.google/trc/about/) (TRC).
-
-**Note**: To avoid any copyright issues, please do not use this model for any purpose.
+This is the Cantonese model of BART base. It is based on another model created by: https://huggingface.co/Ayaka/bart-base-cantonese
 
-## GitHub Links
 
-- Dataset: [ayaka14732/lihkg-scraper](https://github.com/ayaka14732/lihkg-scraper)
-- Tokeniser: [ayaka14732/bert-tokenizer-cantonese](https://github.com/ayaka14732/bert-tokenizer-cantonese)
-- Base model: [ayaka14732/bart-base-jax](https://github.com/ayaka14732/bart-base-jax)
-- Pre-training: [ayaka14732/bart-base-cantonese](https://github.com/ayaka14732/bart-base-cantonese)
 
 ## Usage
 
 ```python
 from transformers import BertTokenizer, BartForConditionalGeneration, Text2TextGenerationPipeline
-tokenizer = BertTokenizer.from_pretrained('Ayaka/bart-base-cantonese')
-model = BartForConditionalGeneration.from_pretrained('Ayaka/bart-base-cantonese')
+tokenizer = BertTokenizer.from_pretrained('jed351/bart-zh-hk-wiki')
+model = BartForConditionalGeneration.from_pretrained('jed351/bart-zh-hk-wiki')
 text2text_generator = Text2TextGenerationPipeline(model, tokenizer)  
 output = text2text_generator('聽日就要返香港，我激動到[MASK]唔着', max_length=50, do_sample=False)
 print(output[0]['generated_text'].replace(' ', ''))
-# output: 聽日就要返香港，我激動到瞓唔着
 ```
 
 **Note**: Please use the `BertTokenizer` for the model vocabulary. DO NOT use the original `BartTokenizer`.