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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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- deberta |
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- deberta-v3 |
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model-index: |
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- name: navteca/deberta-v3-base-squad2 |
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results: |
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- task: |
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type: question-answering |
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name: Question Answering |
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dataset: |
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name: squad_v2 |
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type: squad_v2 |
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config: squad_v2 |
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split: validation |
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metrics: |
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- name: Exact Match |
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type: exact_match |
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value: 88.0876 |
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verified: true |
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- name: F1 |
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type: f1 |
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value: 91.1623 |
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verified: true |
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- task: |
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type: question-answering |
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name: Question Answering |
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dataset: |
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name: squad |
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type: squad |
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config: plain_text |
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split: validation |
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metrics: |
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- name: Exact Match |
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type: exact_match |
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value: 89.2366 |
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verified: true |
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- name: F1 |
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type: f1 |
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value: 95.0569 |
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verified: true |
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--- |
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|
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# Deberta v3 large model for QA (SQuAD 2.0) |
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This is the [deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) model, fine-tuned using the [SQuAD2.0](https://huggingface.co/datasets/squad_v2) dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Question Answering. |
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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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deberta_model = AutoModelForQuestionAnswering.from_pretrained('navteca/deberta-v3-large-squad2') |
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deberta_tokenizer = AutoTokenizer.from_pretrained('navteca/deberta-v3-large-squad2') |
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# Get predictions |
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nlp = pipeline('question-answering', model=deberta_model, tokenizer=deberta_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": 0.96186668, |
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# "start": 27, |
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#} |
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``` |
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## Author |
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[deepset](http://deepset.ai/) |
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