SpanMarker with bert base uncased on Acronym Identification This is a SpanMarker model trained on the Acronym Identification dataset that can be used for Named Entity Recognition. This SpanMarker model uses bert base uncased as the underlying encoder. See train.py for the training script. Is your data always capitalized correctly? Then consider using the cased variant of this model instead for better performance: tomaarsen/span marker bert base acronyms. Model Details Model Description Model Type: SpanMarker Encoder: bert base uncased Maximum Sequence Length: 256 tokens Maximum Entity Length: 8 words Training Dataset: Acronym Identification Language: en License: apache 2.0 Model Sources Repository: SpanMarker on GitHub Thesis: SpanMarker For Named Entity Recognition Model Labels Label Examples : : long "successive convex approximation", "controlled natural language", "Conversational Question Answering" short "SODA", "CNL", "CoQA" Evaluation Metrics Label Precision Recall F1 : : : : all 0.9339 0.9063 0.9199 long 0.9314 0.8845 0.9074 short 0.9352 0.9174 0.9262 Uses Direct Use for Inference Downstream Use You can finetune this model on your own dataset. Click to expand Training Detail…
We use cookies for essential functionality and analytics. You can accept or reject analytics cookies.Cookie policy