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README.md
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- generated_from_trainer
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model-index:
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- name: drug-stance-bert
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results: [1, 0,
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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The model uploaded here is Model I.
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## Intended uses & limitations
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Predict opinions (negative, neutral, and positive) of tweet initiators regarding the use of a drug for the treatment or prevention of the coronavirus.
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The model works better when there's only one drug name in a single tweet.
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## Tutorial
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See our Github repo for [inference scripts](https://github.com/ningkko/COVID-drug/blob/main/stance_detection/inference.ipynb)
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- generated_from_trainer
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model-index:
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- name: drug-stance-bert
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results: [1, 0, 2]
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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The model uploaded here is Model I.
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## Intended uses & limitations
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Predict opinions (negative, neutral, and positive) of tweet initiators regarding the use of a drug for the treatment or prevention of the coronavirus.
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The model works better when there's only one drug name in a single tweet.
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## Inference & understanding
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We followed COVID-CQ to use the following label representation:
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- 0 -> None/Neutral;
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- 1 -> Against;
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- 2 -> Favor
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Try these examples:
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- The gov's killing people by banning Ivm
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- Great news cheers everybody:) ivermectin proven to not work by rct lol
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## Tutorial
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See our Github repo for [inference scripts](https://github.com/ningkko/COVID-drug/blob/main/stance_detection/inference.ipynb)
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