| license: apache-2.0 | |
| datasets: | |
| - squad_v2 | |
| model-index: | |
| - name: nlpconnect/deberta-v3-xsmall-squad2 | |
| results: | |
| - task: | |
| type: question-answering | |
| name: Question Answering | |
| dataset: | |
| name: squad_v2 | |
| type: squad_v2 | |
| config: squad_v2 | |
| split: validation | |
| metrics: | |
| - type: exact_match | |
| value: 79.3917 | |
| name: Exact Match | |
| verified: true | |
| verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZTFiMWI5YzFlMDZhMzc2NDIwYjNiZmIyMThmOWQxYjFjZmM2ZDQ0OGM2NmNlNmI3Y2U2N2JjMmVkZTgyZjNiOCIsInZlcnNpb24iOjF9.MCw9UJ3MI3Lf5hvOgk7Lw2xZfN4678p7ebG3vnGXX_Avw6fELTPwxZ9qGA-9tL00p4NxaSb3Cx6XAFvWetAIBA | |
| - type: f1 | |
| value: 82.6738 | |
| name: F1 | |
| verified: true | |
| verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMjdiYWY2MzU4YjZhMWQzZGJhZTk3NzU3Y2UwYmQ4MzliZmQxOGUxZDllN2Y0ZmZhYjVlNTE0MzY1MjU5OWMwMCIsInZlcnNpb24iOjF9.zeWLwXy77n0YKxGA5gjySe8p-_nPQxbiPnvQU2tF45IyMmlYKUuLeq4hJnNe-5NgriTf8xkBJBE7Cr5lWHy_Cw | |
| - task: | |
| type: question-answering | |
| name: Question Answering | |
| dataset: | |
| name: squad | |
| type: squad | |
| config: plain_text | |
| split: validation | |
| metrics: | |
| - type: exact_match | |
| value: 84.9246 | |
| name: Exact Match | |
| verified: true | |
| verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZGJhYmU0Y2I4Y2UyOGVlOTlkMmQ2OTcyMTZkNTkwNTMzNzhmNzZiYjU4ZDkxMGM5NzAyMjk1M2ExNGIzOWU4NCIsInZlcnNpb24iOjF9.ql1rCId6lQ7Uwq2spG3q2fFppkFGHA1IWQjvyPRhvKdRNzApBO0mu9JjMAv4uNKZX-kmGEkI018_9tAzN7kwDw | |
| - type: f1 | |
| value: 91.6201 | |
| name: F1 | |
| verified: true | |
| verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZjBjMmI0OTFmODVjMzllZDM0NTdmNjU4NGI4NzA4NTJhOWVkMDQ5OTY0MDcyMWEwZTFkODNlY2VhZjU2NWJmZSIsInZlcnNpb24iOjF9.rGvF60bfWIXzB66C7fkdxCtZvRZ_m3onbLaNbs7M4M0Fk27xnMat6IAy1DeTztkOKLoiD2s2NQH6wXid83cgCw | |
| # Deberta-v3-xsmall-squad2 | |
| ## What is SQuAD? | |
| Stanford 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. | |
| SQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions written adversarially by crowdworkers to look similar to answerable ones. To do well on SQuAD2.0, systems must not only answer questions when possible, but also determine when no answer is supported by the paragraph and abstain from answering. | |
| ## Inference | |
| ```python | |
| from transformers import pipeline | |
| qa = pipeline("question-answering", model="nlpconnect/deberta-v3-xsmall-squad2") | |
| result = qa(context="My name is Sarah and I live in London", question="Where do I live?") | |
| ``` | |
| ## Accuracy | |
| ```json | |
| squad_v2 = {'exact': 79.392, | |
| 'f1': 82.674} | |
| squad = {'exact': 84.925, | |
| 'f1': 91.620} | |
| ``` |