Question Answering
Transformers
PyTorch
TensorFlow
JAX
Rust
Safetensors
English
roberta
Eval Results (legacy)
Instructions to use deepset/roberta-base-squad2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deepset/roberta-base-squad2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="deepset/roberta-base-squad2")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("deepset/roberta-base-squad2") model = AutoModelForQuestionAnswering.from_pretrained("deepset/roberta-base-squad2") - Inference
- Notebooks
- Google Colab
- Kaggle
add AIBOM
#32
by fatima113 - opened
- deepset_roberta-base-squad2.json +237 -0
deepset_roberta-base-squad2.json
ADDED
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{
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"bomFormat": "CycloneDX",
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"specVersion": "1.6",
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"serialNumber": "urn:uuid:cef2615a-e860-42b2-b351-7f8a5f49e535",
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"version": 1,
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"metadata": {
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"timestamp": "2025-07-10T08:45:16.787707+00:00",
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"component": {
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"type": "machine-learning-model",
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"bom-ref": "deepset/roberta-base-squad2-12395755-d71a-5489-a970-16cfa514aa95",
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"name": "deepset/roberta-base-squad2",
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"externalReferences": [
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{
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"url": "https://huggingface.co/deepset/roberta-base-squad2",
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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": "question-answering",
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"architectureFamily": "roberta",
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"modelArchitecture": "RobertaForQuestionAnswering",
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"datasets": [
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{
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"ref": "squad_v2-9c72005c-340e-5f42-8f7a-ae0c57af7584"
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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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},
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{
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"name": "base_model",
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"value": "FacebookAI/roberta-base"
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}
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],
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"quantitativeAnalysis": {
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"performanceMetrics": [
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{
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"slice": "dataset: squad_v2, split: validation, config: squad_v2",
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"type": "exact_match",
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"value": 79.9309
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},
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{
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"slice": "dataset: squad_v2, split: validation, config: squad_v2",
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"type": "f1",
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"value": 82.9501
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},
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{
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"slice": "dataset: squad_v2, split: validation, config: squad_v2",
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"type": "total",
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"value": 11869
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},
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{
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"slice": "dataset: squad, split: validation, config: plain_text",
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"type": "exact_match",
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"value": 85.289
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},
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{
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"slice": "dataset: squad, split: validation, config: plain_text",
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"type": "f1",
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"value": 91.841
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},
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{
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"slice": "dataset: adversarial_qa, split: validation, config: adversarialQA",
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"type": "exact_match",
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"value": 29.5
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},
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{
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"slice": "dataset: adversarial_qa, split: validation, config: adversarialQA",
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"type": "f1",
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"value": 40.367
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},
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{
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"slice": "dataset: squad_adversarial, split: validation, config: AddOneSent",
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"type": "exact_match",
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"value": 78.567
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},
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{
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"slice": "dataset: squad_adversarial, split: validation, config: AddOneSent",
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"type": "f1",
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"value": 84.469
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},
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{
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"slice": "dataset: squadshifts, split: test, config: amazon",
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"type": "exact_match",
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"value": 69.924
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},
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{
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"slice": "dataset: squadshifts, split: test, config: amazon",
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"type": "f1",
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"value": 83.284
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},
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{
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"slice": "dataset: squadshifts, split: test, config: new_wiki",
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"type": "exact_match",
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"value": 81.204
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},
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{
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"slice": "dataset: squadshifts, split: test, config: new_wiki",
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"type": "f1",
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"value": 90.595
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},
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{
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"slice": "dataset: squadshifts, split: test, config: nyt",
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"type": "exact_match",
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"value": 82.931
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},
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{
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"slice": "dataset: squadshifts, split: test, config: nyt",
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"type": "f1",
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"value": 90.756
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},
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{
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"slice": "dataset: squadshifts, split: test, config: reddit",
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"type": "exact_match",
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"value": 71.55
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},
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{
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"slice": "dataset: squadshifts, split: test, config: reddit",
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"type": "f1",
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"value": 82.939
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}
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]
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}
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},
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"authors": [
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{
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"name": "deepset"
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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": "CC-BY-4.0",
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"url": "https://spdx.org/licenses/CC-BY-4.0.html"
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}
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}
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],
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"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",
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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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"rust",
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"safetensors",
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"roberta",
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"question-answering",
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"en",
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| 153 |
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"dataset:squad_v2",
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| 154 |
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"base_model:FacebookAI/roberta-base",
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| 155 |
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"base_model:finetune:FacebookAI/roberta-base",
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| 156 |
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"license:cc-by-4.0",
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| 157 |
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"model-index",
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| 158 |
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"endpoints_compatible",
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| 159 |
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"region:us"
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]
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| 161 |
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}
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},
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"components": [
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| 164 |
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{
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| 165 |
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"type": "data",
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| 166 |
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"bom-ref": "squad_v2-9c72005c-340e-5f42-8f7a-ae0c57af7584",
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| 167 |
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"name": "squad_v2",
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| 168 |
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"data": [
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| 169 |
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{
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| 170 |
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"type": "dataset",
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| 171 |
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"bom-ref": "squad_v2-9c72005c-340e-5f42-8f7a-ae0c57af7584",
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| 172 |
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"name": "squad_v2",
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| 173 |
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"contents": {
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| 174 |
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"url": "https://huggingface.co/datasets/squad_v2",
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"properties": [
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{
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"name": "task_categories",
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"value": "question-answering"
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},
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{
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"name": "task_ids",
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"value": "open-domain-qa, extractive-qa"
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},
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{
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"name": "language",
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"value": "en"
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},
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{
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| 189 |
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"name": "size_categories",
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| 190 |
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"value": "100K<n<1M"
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},
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{
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| 193 |
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"name": "annotations_creators",
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"value": "crowdsourced"
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},
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{
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"name": "language_creators",
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"value": "crowdsourced"
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},
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| 200 |
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{
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| 201 |
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"name": "pretty_name",
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| 202 |
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"value": "SQuAD2.0"
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| 203 |
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},
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| 204 |
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{
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| 205 |
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"name": "source_datasets",
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| 206 |
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"value": "original"
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},
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{
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| 209 |
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"name": "paperswithcode_id",
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"value": "squad"
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},
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| 212 |
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{
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"name": "configs",
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"value": "Name of the dataset subset: squad_v2 {\"split\": \"train\", \"path\": \"squad_v2/train-*\"}, {\"split\": \"validation\", \"path\": \"squad_v2/validation-*\"}"
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},
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| 216 |
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{
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| 217 |
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"name": "license",
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| 218 |
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"value": "cc-by-sa-4.0"
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}
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]
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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": "rajpurkar",
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"url": "https://huggingface.co/rajpurkar"
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}
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}
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]
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},
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"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."
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}
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]
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}
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]
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}
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