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@@ -48,7 +48,7 @@ evaluation_results:
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  ## Model Overview โณ๐Ÿ”ฎ๐Ÿ”„
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- This model is a **text classification model** trained to **predict the tense of English sentences**: **โช Past**, **โบ๏ธ Present**, or **โฉFuture**. It is based on the `bert-base-uncased` architecture.
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  ## Intended Use ๐Ÿ”
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@@ -61,9 +61,9 @@ This model can be used in applications such as:
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  | Sentence | Label |
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  |--------------------------------------------------------------------------|---------|
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- | the fishermen had caught a variety of fish including bass and perch | Past โช |
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- | medical professionals are researching the impact of social determinants on health | Present โบ๏ธ |
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- | in the future robotic surgical systems will have been empowering surgeons to perform increasingly complex procedures | Future โฉ |
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  ## Training Details ๐Ÿ‹๏ธโ€โ™‚๏ธ
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  ## Model Overview โณ๐Ÿ”ฎ๐Ÿ”„
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+ This model is a **text classification model** trained to **predict the tense of English sentences**: **Past**, **Present**, or **Future**. It is based on the `bert-base-uncased` architecture.
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  ## Intended Use ๐Ÿ”
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  | Sentence | Label |
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  |--------------------------------------------------------------------------|---------|
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+ | the fishermen had caught a variety of fish including bass and perch | Past |
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+ | medical professionals are researching the impact of social determinants on health | Present |
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+ | in the future robotic surgical systems will have been empowering surgeons to perform increasingly complex procedures | Future |
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  ## Training Details ๐Ÿ‹๏ธโ€โ™‚๏ธ
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