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| "metadata": { | |
| "timestamp": "2025-07-10T08:45:16.787707+00:00", | |
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| "type": "machine-learning-model", | |
| "bom-ref": "deepset/roberta-base-squad2-12395755-d71a-5489-a970-16cfa514aa95", | |
| "name": "deepset/roberta-base-squad2", | |
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| { | |
| "url": "https://huggingface.co/deepset/roberta-base-squad2", | |
| "type": "documentation" | |
| } | |
| ], | |
| "modelCard": { | |
| "modelParameters": { | |
| "task": "question-answering", | |
| "architectureFamily": "roberta", | |
| "modelArchitecture": "RobertaForQuestionAnswering", | |
| "datasets": [ | |
| { | |
| "ref": "squad_v2-9c72005c-340e-5f42-8f7a-ae0c57af7584" | |
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| "properties": [ | |
| { | |
| "name": "library_name", | |
| "value": "transformers" | |
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| { | |
| "name": "base_model", | |
| "value": "FacebookAI/roberta-base" | |
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| ], | |
| "quantitativeAnalysis": { | |
| "performanceMetrics": [ | |
| { | |
| "slice": "dataset: squad_v2, split: validation, config: squad_v2", | |
| "type": "exact_match", | |
| "value": 79.9309 | |
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| "type": "f1", | |
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| { | |
| "slice": "dataset: squad_v2, split: validation, config: squad_v2", | |
| "type": "total", | |
| "value": 11869 | |
| }, | |
| { | |
| "slice": "dataset: squad, split: validation, config: plain_text", | |
| "type": "exact_match", | |
| "value": 85.289 | |
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| { | |
| "slice": "dataset: squad, split: validation, config: plain_text", | |
| "type": "f1", | |
| "value": 91.841 | |
| }, | |
| { | |
| "slice": "dataset: adversarial_qa, split: validation, config: adversarialQA", | |
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| { | |
| "slice": "dataset: adversarial_qa, split: validation, config: adversarialQA", | |
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| "value": 40.367 | |
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| { | |
| "slice": "dataset: squad_adversarial, split: validation, config: AddOneSent", | |
| "type": "exact_match", | |
| "value": 78.567 | |
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| { | |
| "slice": "dataset: squad_adversarial, split: validation, config: AddOneSent", | |
| "type": "f1", | |
| "value": 84.469 | |
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| "slice": "dataset: squadshifts, split: test, config: amazon", | |
| "type": "exact_match", | |
| "value": 69.924 | |
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| "type": "f1", | |
| "value": 83.284 | |
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| { | |
| "slice": "dataset: squadshifts, split: test, config: new_wiki", | |
| "type": "exact_match", | |
| "value": 81.204 | |
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| { | |
| "slice": "dataset: squadshifts, split: test, config: new_wiki", | |
| "type": "f1", | |
| "value": 90.595 | |
| }, | |
| { | |
| "slice": "dataset: squadshifts, split: test, config: nyt", | |
| "type": "exact_match", | |
| "value": 82.931 | |
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| { | |
| "slice": "dataset: squadshifts, split: test, config: nyt", | |
| "type": "f1", | |
| "value": 90.756 | |
| }, | |
| { | |
| "slice": "dataset: squadshifts, split: test, config: reddit", | |
| "type": "exact_match", | |
| "value": 71.55 | |
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| { | |
| "slice": "dataset: squadshifts, split: test, config: reddit", | |
| "type": "f1", | |
| "value": 82.939 | |
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| ] | |
| } | |
| }, | |
| "authors": [ | |
| { | |
| "name": "deepset" | |
| } | |
| ], | |
| "licenses": [ | |
| { | |
| "license": { | |
| "id": "CC-BY-4.0", | |
| "url": "https://spdx.org/licenses/CC-BY-4.0.html" | |
| } | |
| } | |
| ], | |
| "description": "**Language model:** roberta-base**Language:** English**Downstream-task:** Extractive QA**Training data:** SQuAD 2.0**Eval data:** SQuAD 2.0**Code:** See [an example extractive QA pipeline built with Haystack](https://haystack.deepset.ai/tutorials/34_extractive_qa_pipeline)**Infrastructure**: 4x Tesla v100", | |
| "tags": [ | |
| "transformers", | |
| "pytorch", | |
| "tf", | |
| "jax", | |
| "rust", | |
| "safetensors", | |
| "roberta", | |
| "question-answering", | |
| "en", | |
| "dataset:squad_v2", | |
| "base_model:FacebookAI/roberta-base", | |
| "base_model:finetune:FacebookAI/roberta-base", | |
| "license:cc-by-4.0", | |
| "model-index", | |
| "endpoints_compatible", | |
| "region:us" | |
| ] | |
| } | |
| }, | |
| "components": [ | |
| { | |
| "type": "data", | |
| "bom-ref": "squad_v2-9c72005c-340e-5f42-8f7a-ae0c57af7584", | |
| "name": "squad_v2", | |
| "data": [ | |
| { | |
| "type": "dataset", | |
| "bom-ref": "squad_v2-9c72005c-340e-5f42-8f7a-ae0c57af7584", | |
| "name": "squad_v2", | |
| "contents": { | |
| "url": "https://huggingface.co/datasets/squad_v2", | |
| "properties": [ | |
| { | |
| "name": "task_categories", | |
| "value": "question-answering" | |
| }, | |
| { | |
| "name": "task_ids", | |
| "value": "open-domain-qa, extractive-qa" | |
| }, | |
| { | |
| "name": "language", | |
| "value": "en" | |
| }, | |
| { | |
| "name": "size_categories", | |
| "value": "100K<n<1M" | |
| }, | |
| { | |
| "name": "annotations_creators", | |
| "value": "crowdsourced" | |
| }, | |
| { | |
| "name": "language_creators", | |
| "value": "crowdsourced" | |
| }, | |
| { | |
| "name": "pretty_name", | |
| "value": "SQuAD2.0" | |
| }, | |
| { | |
| "name": "source_datasets", | |
| "value": "original" | |
| }, | |
| { | |
| "name": "paperswithcode_id", | |
| "value": "squad" | |
| }, | |
| { | |
| "name": "configs", | |
| "value": "Name of the dataset subset: squad_v2 {\"split\": \"train\", \"path\": \"squad_v2/train-*\"}, {\"split\": \"validation\", \"path\": \"squad_v2/validation-*\"}" | |
| }, | |
| { | |
| "name": "license", | |
| "value": "cc-by-sa-4.0" | |
| } | |
| ] | |
| }, | |
| "governance": { | |
| "owners": [ | |
| { | |
| "organization": { | |
| "name": "rajpurkar", | |
| "url": "https://huggingface.co/rajpurkar" | |
| } | |
| } | |
| ] | |
| }, | |
| "description": "\n\t\n\t\t\n\t\tDataset Card for SQuAD 2.0\n\t\n\n\n\t\n\t\t\n\t\tDataset Summary\n\t\n\nStanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable.\nSQuAD 2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions written adversarially by crowdworkers\u2026 See the full description on the dataset page: https://huggingface.co/datasets/rajpurkar/squad_v2." | |
| } | |
| ] | |
| } | |
| ] | |
| } |