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  - en
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  pipeline_tag: text2text-generation
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  ---
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- # Model Card for Model ID
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  ## Model Details
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- ### Model Description
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-
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  - **Model type:** Text-to-Text Generation
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  - **Language(s) (NLP):** English
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  - **License:** MIT License
@@ -26,22 +24,14 @@ from transformers import pipeline
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  pipe = pipeline("text2text-generation", model="textgain/News2Topic-T5-base")
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- ```
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- # Example usage
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-
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- ```
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  news_text = "Your news text here."
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  print(pipe(news_text))
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  ```
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  ## Training Details
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- ### Training Data
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-
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  The News2Topic T5-base model was trained on a 21K sample of the "newsroom" dataset annotated with synthetic data generated by GPT-3.5-turbo
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- ### Training Procedure
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  The model was trained for 3 epochs, with a learning rate of 0.00001, a maximum sequence length of 512, and a training batch size of 12.
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  ## Citation
 
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  - en
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  pipeline_tag: text2text-generation
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  ---
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+ # News2Topic-T5-base
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  ## Model Details
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  - **Model type:** Text-to-Text Generation
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  - **Language(s) (NLP):** English
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  - **License:** MIT License
 
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  pipe = pipeline("text2text-generation", model="textgain/News2Topic-T5-base")
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  news_text = "Your news text here."
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  print(pipe(news_text))
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  ```
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  ## Training Details
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  The News2Topic T5-base model was trained on a 21K sample of the "newsroom" dataset annotated with synthetic data generated by GPT-3.5-turbo
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  The model was trained for 3 epochs, with a learning rate of 0.00001, a maximum sequence length of 512, and a training batch size of 12.
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  ## Citation