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--- |
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datasets: |
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- squad_v2 |
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language: en |
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license: mit |
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pipeline_tag: question-answering |
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tags: |
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- electra |
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- question-answering |
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--- |
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# Electra base model for QA (SQuAD 2.0) |
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This model uses [electra-base](https://huggingface.co/google/electra-base-discriminator). |
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## Training Data |
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The models have been trained on the [SQuAD 2.0](https://rajpurkar.github.io/SQuAD-explorer/) dataset. |
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It can be used for question answering task. |
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## Usage and Performance |
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The trained model can be used like this: |
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```python |
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from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline |
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# Load model & tokenizer |
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electra_model = AutoModelForQuestionAnswering.from_pretrained('navteca/electra-base-squad2') |
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electra_tokenizer = AutoTokenizer.from_pretrained('navteca/electra-base-squad2') |
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# Get predictions |
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nlp = pipeline('question-answering', model=electra_model, tokenizer=electra_tokenizer) |
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result = nlp({ |
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'question': 'How many people live in Berlin?', |
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'context': 'Berlin had a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.' |
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}) |
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print(result) |
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#{ |
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# "answer": "3,520,031" |
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# "end": 36, |
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# "score": 0.99983448, |
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# "start": 27, |
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#} |
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``` |
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