Tomaarsen
Rank:Average Model Cost: $0.0000
Number of Runs: 7,507
Models by this creator
span-marker-bert-tiny-conll03
span-marker-bert-tiny-conll03
The span-marker-bert-tiny-conll03 model is a Named Entity Recognition (NER) model that uses the SpanMarker library. It is based on the prajjwal1/bert-tiny model and is used to identify and classify named entities in text. To use the model, you need to install the span_marker library and then run inference with the model. For more details and documentation, refer to the SpanMarker repository.
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6.0K
Huggingface
span-marker-roberta-large-ontonotes5
span-marker-roberta-large-ontonotes5
SpanMarker for Named Entity Recognition This is a SpanMarker model that can be used for Named Entity Recognition. In particular, this SpanMarker model uses roberta-large as the underlying encoder. See train.py for the training script. Usage To use this model for inference, first install the span_marker library: You can then run inference with this model like so: See the SpanMarker repository for documentation and additional information on this library.
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616
Huggingface
span-marker-bert-base-fewnerd-fine-super
span-marker-bert-base-fewnerd-fine-super
SpanMarker for Named Entity Recognition This is a SpanMarker model that can be used for Named Entity Recognition. In particular, this SpanMarker model uses bert-base-cased as the underlying encoder. Usage To use this model for inference, first install the span_marker library: You can then run inference with this model like so: See the SpanMarker repository for documentation and additional information on this library.
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295
Huggingface
span-marker-bert-tiny-fewnerd-coarse-super
span-marker-bert-tiny-fewnerd-coarse-super
SpanMarker for Named Entity Recognition This is a SpanMarker model that can be used for Named Entity Recognition. In particular, this SpanMarker model uses prajjwal1/bert-tiny as the underlying encoder. Usage To use this model for inference, first install the span_marker library: You can then run inference with this model like so: See the SpanMarker repository for documentation and additional information on this library.
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281
Huggingface
span-marker-xlm-roberta-large-conll03
span-marker-xlm-roberta-large-conll03
SpanMarker for Named Entity Recognition This is a SpanMarker model that can be used for Named Entity Recognition. In particular, this SpanMarker model uses xlm-roberta-large as the underlying encoder. See train.py for the training script. Usage To use this model for inference, first install the span_marker library: You can then run inference with this model like so: See the SpanMarker repository for documentation and additional information on this library.
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62
Huggingface
span-marker-xlm-roberta-base-fewnerd-fine-super
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59
Huggingface
span-marker-xlm-roberta-large-conll03-doc-context
span-marker-xlm-roberta-large-conll03-doc-context
SpanMarker for Named Entity Recognition This is a SpanMarker model that can be used for Named Entity Recognition. In particular, this SpanMarker model uses xlm-roberta-large as the underlying encoder. See train.py for the training script. Note that this model was trained with document-level context, i.e. it will primarily perform well when provided with enough context. It is recommended to call model.predict with a 🤗 Dataset with tokens, document_id and sentence_id columns. See the documentation of the model.predict method for more information. Usage To use this model for inference, first install the span_marker library: You can then run inference with this model like so: See the SpanMarker repository for documentation and additional information on this library.
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58
Huggingface
span-marker-roberta-large-fewnerd-fine-super
span-marker-roberta-large-fewnerd-fine-super
Platform did not provide a description for this model.
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53
Huggingface
span-marker-xlm-roberta-large-conllpp-doc-context
span-marker-xlm-roberta-large-conllpp-doc-context
SpanMarker for Named Entity Recognition This is a SpanMarker model that can be used for Named Entity Recognition. In particular, this SpanMarker model uses xlm-roberta-large as the underlying encoder. See train.py for the training script. Note that this model was trained with document-level context, i.e. it will primarily perform well when provided with enough context. It is recommended to call model.predict with a 🤗 Dataset with tokens, document_id and sentence_id columns. See the documentation of the model.predict method for more information. Usage To use this model for inference, first install the span_marker library: You can then run inference with this model like so: See the SpanMarker repository for documentation and additional information on this library.
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53
Huggingface