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Add README.md
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README.md
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@@ -31,11 +31,11 @@ was pretrained with two objectives:
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predict if the two sentences were following each other or not.
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This way, the model learns an inner representation of the English language that can then be used to extract features
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useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard
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classifier using the features produced by the
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## Intended uses & limitations
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You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to
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be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=
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fine-tuned versions on a task that interests you.
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Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
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to make decisions, such as sequence classification, token classification or question answering. For tasks such as text
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predict if the two sentences were following each other or not.
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This way, the model learns an inner representation of the English language that can then be used to extract features
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useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard
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classifier using the features produced by the MultiBERTs model as inputs.
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## Intended uses & limitations
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You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to
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38 |
+
be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=multiberts) to look for
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fine-tuned versions on a task that interests you.
|
40 |
Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
|
41 |
to make decisions, such as sequence classification, token classification or question answering. For tasks such as text
|