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| --- |
| language: |
| - en |
| license: "apache-2.0" |
| datasets: |
| - CoNLL-2003 |
| metrics: |
| - F1 |
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| --- |
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| This is a T5 small model finetuned on CoNLL-2003 dataset for named entity recognition (NER). |
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| Example Input and Output: |
| “Recognize all the named entities in this sequence (replace named entities with one of [PER], [ORG], [LOC], [MISC]): When Alice visited New York” → “When PER visited LOC LOC" |
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| Evaluation Result: |
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| % of match (for comparison with ExT5: https://arxiv.org/pdf/2111.10952.pdf): |
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| | Model| ExT5_{Base} | This Model | T5_NER_CONLL_OUTPUTLIST |
| | :---: | :---: | :---: | :---: | |
| | % of Complete Match| 86.53 | 79.03 | TBA| |
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| There are some outputs (212/3453 or 6.14% that does not have the same length as the input) |
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| F1 score on testing set of those with matching length : |
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| | Model | This Model | T5_NER_CONLL_OUTPUTLIST | BERTbase |
| | :---: | :---: | :---: | :---: | |
| | F1| 0.8901 | 0.8691| 0.9240 |
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| **Caveat: The testing set of these aren't the same, due to matching length issue... |
| T5_NER_CONLL_OUTPUTLIST only has 27/3453 missing length (only 0.78%); The BERT number is directly from their paper (https://arxiv.org/pdf/1810.04805.pdf) |
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