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  1. dslim_bert-base-NER.json +169 -0
dslim_bert-base-NER.json ADDED
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+ "version": 1,
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+ "metadata": {
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+ "timestamp": "2025-06-05T09:39:43.530276+00:00",
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+ "component": {
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+ "type": "machine-learning-model",
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+ "bom-ref": "dslim/bert-base-NER-40551b74-59a4-53a9-ae36-e1dca4f66e41",
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+ "name": "dslim/bert-base-NER",
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+ "externalReferences": [
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+ {
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+ "url": "https://huggingface.co/dslim/bert-base-NER",
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+ "type": "documentation"
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+ }
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+ ],
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+ "modelCard": {
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+ "modelParameters": {
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+ "task": "token-classification",
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+ "architectureFamily": "bert",
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+ "modelArchitecture": "BertForTokenClassification",
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+ "datasets": [
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+ {
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+ "ref": "conll2003-be67a053-25af-52ad-93c8-134501f8fa4b"
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+ }
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+ ]
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+ },
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+ "properties": [
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+ {
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+ "name": "library_name",
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+ "value": "transformers"
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+ "quantitativeAnalysis": {
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+ "performanceMetrics": [
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+ {
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+ "slice": "dataset: conll2003, split: test, config: conll2003",
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+ "type": "accuracy",
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+ "value": 0.9118041001560013
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+ {
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+ "slice": "dataset: conll2003, split: test, config: conll2003",
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+ "type": "precision",
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+ "slice": "dataset: conll2003, split: test, config: conll2003",
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+ "type": "recall",
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+ "value": 0.9306415698281261
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+ {
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+ "slice": "dataset: conll2003, split: test, config: conll2003",
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+ "type": "f1",
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+ "value": 0.9258740048459675
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+ {
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+ "slice": "dataset: conll2003, split: test, config: conll2003",
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+ "value": 0.48325642943382263
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+ "authors": [
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+ {
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+ "name": "dslim"
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+ }
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+ ],
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+ "licenses": [
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+ {
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+ "license": {
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+ "id": "MIT",
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+ "url": "https://spdx.org/licenses/MIT.html"
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+ }
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+ }
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+ ],
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+ "description": "**bert-base-NER** is a fine-tuned BERT model that is ready to use for **Named Entity Recognition** and achieves **state-of-the-art performance** for the NER task. It has been trained to recognize four types of entities: location (LOC), organizations (ORG), person (PER) and Miscellaneous (MISC).Specifically, this model is a *bert-base-cased* model that was fine-tuned on the English version of the standard [CoNLL-2003 Named Entity Recognition](https://www.aclweb.org/anthology/W03-0419.pdf) dataset.If you'd like to use a larger BERT-large model fine-tuned on the same dataset, a [**bert-large-NER**](https://huggingface.co/dslim/bert-large-NER/) version is also available.",
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+ "tags": [
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+ "transformers",
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+ "pytorch",
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+ "tf",
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+ "jax",
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+ "onnx",
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+ "safetensors",
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+ "bert",
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+ "token-classification",
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+ "en",
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+ "dataset:conll2003",
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+ "arxiv:1810.04805",
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+ "license:mit",
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+ "model-index",
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+ "autotrain_compatible",
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+ "endpoints_compatible",
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+ "region:us"
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+ ]
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+ }
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+ },
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+ "components": [
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+ "type": "data",
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+ "bom-ref": "conll2003-be67a053-25af-52ad-93c8-134501f8fa4b",
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+ "name": "conll2003",
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+ "data": [
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+ {
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+ "type": "dataset",
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+ "bom-ref": "conll2003-be67a053-25af-52ad-93c8-134501f8fa4b",
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+ "name": "conll2003",
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+ "contents": {
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+ "url": "https://huggingface.co/datasets/conll2003",
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+ "properties": [
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+ {
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+ "name": "task_categories",
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+ "value": "token-classification"
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+ },
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+ {
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+ "name": "task_ids",
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+ "value": "named-entity-recognition, part-of-speech"
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+ },
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+ {
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+ "name": "language",
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+ "value": "en"
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+ "name": "size_categories",
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+ "value": "10K<n<100K"
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+ "name": "annotations_creators",
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+ "value": "crowdsourced"
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+ {
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+ "name": "language_creators",
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+ "value": "found"
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+ "name": "pretty_name",
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+ "value": "CoNLL-2003"
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+ "name": "source_datasets",
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+ "value": "extended|other-reuters-corpus"
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+ },
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+ {
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+ "name": "paperswithcode_id",
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+ "value": "conll-2003"
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+ },
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+ {
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+ "name": "license",
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+ "value": "other"
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+ }
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+ "governance": {
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+ "owners": [
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+ {
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+ "organization": {
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+ "name": "eriktks",
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+ "url": "https://huggingface.co/eriktks"
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+ }
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+ }
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+ ]
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+ },
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+ "description": "The shared task of CoNLL-2003 concerns language-independent named entity recognition. We will concentrate on\nfour types of named entities: persons, locations, organizations and names of miscellaneous entities that do\nnot belong to the previous three groups.\n\nThe CoNLL-2003 shared task data files contain four columns separated by a single space. Each word has been put on\na separate line and there is an empty line after each sentence. The first item on each line is a word, the second\na part-of-speech (POS) tag, the third a syntactic chunk tag and the fourth the named entity tag. The chunk tags\nand the named entity tags have the format I-TYPE which means that the word is inside a phrase of type TYPE. Only\nif two phrases of the same type immediately follow each other, the first word of the second phrase will have tag\nB-TYPE to show that it starts a new phrase. A word with tag O is not part of a phrase. Note the dataset uses IOB2\ntagging scheme, whereas the original dataset uses IOB1.\n\nFor more details see https://www.clips.uantwerpen.be/conll2003/ner/ and https://www.aclweb.org/anthology/W03-0419"
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+ }
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+ ]
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+ }
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+ ]
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+ }