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@@ -48,14 +48,12 @@ classifier = \
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  pipeline(model="OpenAlex/bert-base-multilingual-cased-finetuned-openalex-topic-classification-title-abstract", top_k=10)
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  classifier(f"""<TITLE> {title}\n<ABSTRACT> {abstract}""")
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-
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  This will return the top 10 outputs from the model. There will be 2 pieces of information here:
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  1. Full Topic Label: Made up of both the [OpenAlex](https://openalex.org/) topic ID and the topic label (ex: "1048: Ecology and Evolution of Viruses in Ecosystems")
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  2. Model Score: Model's confidence in the topic (ex: "0.364")
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- ```
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-
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  ## Intended uses & limitations
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  The model is intended to be used as part of a larger model that also incorporates journal information and citation features. However, this model is good if you want to use it for quickly generating a topic based only on a title/abstract.
 
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  pipeline(model="OpenAlex/bert-base-multilingual-cased-finetuned-openalex-topic-classification-title-abstract", top_k=10)
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  classifier(f"""<TITLE> {title}\n<ABSTRACT> {abstract}""")
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+ ```
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  This will return the top 10 outputs from the model. There will be 2 pieces of information here:
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  1. Full Topic Label: Made up of both the [OpenAlex](https://openalex.org/) topic ID and the topic label (ex: "1048: Ecology and Evolution of Viruses in Ecosystems")
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  2. Model Score: Model's confidence in the topic (ex: "0.364")
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  ## Intended uses & limitations
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  The model is intended to be used as part of a larger model that also incorporates journal information and citation features. However, this model is good if you want to use it for quickly generating a topic based only on a title/abstract.