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--- |
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pipeline_tag: feature-extraction |
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tags: |
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- feature-extraction |
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- transformers |
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license: apache-2.0 |
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language: |
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- id |
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metrics: |
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- accuracy |
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- f1 |
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- precision |
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- recall |
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datasets: |
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- squad_v2 |
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--- |
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### indo-dpr-question_encoder-single-squad-base |
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<p style="font-size:16px">Indonesian Dense Passage Retrieval trained on translated SQuADv2.0 dataset in DPR format.</p> |
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### Evaluation |
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| Class | Precision | Recall | F1-Score | Support | |
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|-------|-----------|--------|----------|---------| |
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| hard_negative | 0.9963 | 0.9963 | 0.9963 | 183090 | |
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| positive | 0.8849 | 0.8849 | 0.8849 | 5910 | |
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| Metric | Value | |
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|--------|-------| |
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| Accuracy | 0.9928 | |
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| Macro Average | 0.9406 | |
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| Weighted Average | 0.9928 | |
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<p style="font-size:16px">Note: This report is for evaluation on the dev set, after 12000 batches.</p> |
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### Usage |
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```python |
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from transformers import DPRQuestionEncoder, DPRQuestionEncoderTokenizer |
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tokenizer = DPRQuestionEncoderTokenizer.from_pretrained('firqaaa/indo-dpr-question_encoder-single-squad-base') |
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model = DPRQuestionEncoder.from_pretrained('firqaaa/indo-dpr-question_encoder-single-squad-base') |
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input_ids = tokenizer("Ibukota Indonesia terletak dimana?", return_tensors='pt')["input_ids"] |
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embeddings = model(input_ids).pooler_output |
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``` |
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We can use it using `haystack` as follows: |
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``` |
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from haystack.nodes import DensePassageRetriever |
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from haystack.document_stores import InMemoryDocumentStore |
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retriever = DensePassageRetriever(document_store=InMemoryDocumentStore(), |
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query_embedding_model="firqaaa/indo-dpr-question_encoder-single-squad-base", |
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passage_embedding_model="firqaaa/indo-dpr-question_encoder-single-squad-base", |
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max_seq_len_query=64, |
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max_seq_len_passage=256, |
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batch_size=16, |
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use_gpu=True, |
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embed_title=True, |
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use_fast_tokenizers=True) |
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``` |
