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README.md
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# Adapter `allenai/
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An [adapter](https://adapterhub.ml) for the `allenai/
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This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
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<!-- Provide a quick summary of what the model is/does. -->
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SPECTER 2.0 is the successor to [SPECTER](allenai/specter) and is capable of generating task specific embeddings for scientific tasks when paired with [adapters](https://huggingface.co/models?search=allenai/specter-2).
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Given the combination of title and abstract of a scientific paper or a short texual query, the model can be used to generate effective embeddings to be used in downstream applications.
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# Model Details
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|Model|Name and HF link|Description|
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|Retrieval*|[allenai/specter2_proximity](https://huggingface.co/allenai/
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|Adhoc Query|[allenai/specter2_adhoc_query](https://huggingface.co/allenai/specter2_adhoc_query)|Encode short raw text queries for search tasks. (Candidate papers can be encoded with proximity)|
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|Classification|[allenai/specter2_classification](https://huggingface.co/allenai/specter2_classification)|Encode papers to feed into linear classifiers as features|
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|Regression|[allenai/specter2_regression](https://huggingface.co/allenai/specter2_regression)|Encode papers to feed into linear regressors as features|
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- allenai/scirepeval
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# Adapter `allenai/specter2` for allenai/specter2_base
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An [adapter](https://adapterhub.ml) for the `allenai/specter2_base` model that was trained on the [allenai/scirepeval](https://huggingface.co/datasets/allenai/scirepeval/) dataset.
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This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
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<!-- Provide a quick summary of what the model is/does. -->
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SPECTER 2.0 is the successor to [SPECTER](allenai/specter) and is capable of generating task specific embeddings for scientific tasks when paired with [adapters](https://huggingface.co/models?search=allenai/specter-2).
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This is the proximity adapter and should be used for all general embedding purposes.
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Given the combination of title and abstract of a scientific paper or a short texual query, the model can be used to generate effective embeddings to be used in downstream applications.
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# Model Details
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|Model|Name and HF link|Description|
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|--|--|--|
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|Retrieval*|[allenai/specter2_proximity](https://huggingface.co/allenai/specter2)|Encode papers as queries and candidates eg. Link Prediction, Nearest Neighbor Search|
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|Adhoc Query|[allenai/specter2_adhoc_query](https://huggingface.co/allenai/specter2_adhoc_query)|Encode short raw text queries for search tasks. (Candidate papers can be encoded with proximity)|
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|Classification|[allenai/specter2_classification](https://huggingface.co/allenai/specter2_classification)|Encode papers to feed into linear classifiers as features|
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|Regression|[allenai/specter2_regression](https://huggingface.co/allenai/specter2_regression)|Encode papers to feed into linear regressors as features|
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