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