metadata
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
Indonesian Dense Passage Retrieval trained on translated SQuADv2.0 dataset in DPR format.
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 |
Note: This report is for evaluation on the dev set, after 12000 batches.
Usage
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)