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## Model Summary
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This model is a fine-tuned version of **albert-base-v2**, designed to classify whether Steam game reviews are constructive or non-constructive. The model was trained on the [1.5K Steam Reviews Binary Labeled for Constructiveness dataset](https://huggingface.co/datasets/abullard1/steam-reviews-constructiveness-binary-label-annotations-1.5k), which consists of user-generated game reviews (along other features) labeled with binary labels (`1 for constructive` or `0 for non-constructive`).
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The datasets featues were concatenated into Strings with the following format: "Review: **{review}**, Playtime: **{author_playtime_at_review}**, Voted Up: **{voted_up}**, Upvotes: **{votes_up}**, Votes Funny: **{votes_funny}**" and then fed to the model accompanied by the respective ***constructive*** labels. This approach of concatenating the features into a simple String offers a good trade-off between complexity and performance, compared to other options.
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### Intended Use
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## Model Summary
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This model is a fine-tuned version of **albert-base-v2**, designed to classify whether Steam game reviews are constructive or non-constructive. The model was trained on the [1.5K Steam Reviews Binary Labeled for Constructiveness dataset](https://huggingface.co/datasets/abullard1/steam-reviews-constructiveness-binary-label-annotations-1.5k), which consists of user-generated game reviews (along other features) labeled with binary labels (`1 for constructive` or `0 for non-constructive`).
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The datasets featues were concatenated into Strings with the following format: "Review: **{review}**, Playtime: **{author_playtime_at_review}**, Voted Up: **{voted_up}**, Upvotes: **{votes_up}**, Votes Funny: **{votes_funny}**" and then fed to the model accompanied by the respective ***constructive*** labels. This approach of concatenating the features into a simple String offers a good trade-off between complexity and performance, compared to other options.
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### Intended Use
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