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license: afl-3.0 |
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About : |
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This model can be used for text summarization. |
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The dataset on which it was fine tuned consisted of 10,323 articles. |
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The Data Fields : |
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- "Headline" : title of the article |
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- "articleBody" : the main article content |
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- "source" : the link to the readmore page. |
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The data splits were : |
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- Train : 8258. |
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- Vaildation : 2065. |
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### How to use along with pipeline |
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```python |
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from transformers import pipeline |
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from transformers import AutoTokenizer, AutoModelForSeq2Seq |
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tokenizer = AutoTokenizer.from_pretrained("AkashKhamkar/InSumT510k") |
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model = AutoModelForSeq2SeqLM.from_pretrained("AkashKhamkar/InSumT510k") |
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summarizer = pipeline("summarization", model=model, tokenizer=tokenizer) |
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summarizer("Text for summarization...", min_length=5, max_length=50) |
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``` |
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language: |
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- English |
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library_name: Pytorch |
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tags: |
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- Summarization |
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- T5-base |
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- Conditional Modelling |
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