metadata
pipeline_tag: feature-extraction
tags:
- feature-extraction
- transformers
license: apache-2.0
language:
- id
metrics:
- accuracy
- f1
- precision
- recall
datasets:
- squad_v2
- natural_questions
indo-dpr-question_encoder-multiset-base
Indonesian Dense Passage Retrieval trained on translated SQuADv2.0 and Natural Question dataset in DPR format.
Evaluation
| Class | Precision | Recall | F1-Score | Support |
|---|---|---|---|---|
| hard_negative | 0.9961 | 0.9961 | 0.9961 | 384778 |
| positive | 0.8783 | 0.8783 | 0.8783 | 12414 |
| Metric | Value |
|---|---|
| Loss | 0.0220 |
| Accuracy | 0.9924 |
| Macro Average | 0.9372 |
| Weighted Average | 0.9924 |
| Accuracy and F1 | 0.9353 |
| Average Rank | 0.2194 |
Note: This report is for evaluation on the dev set, after 27288 batches.
Usage
from transformers import DPRQuestionEncoder, DPRQuestionEncoderTokenizer
tokenizer = DPRQuestionEncoderTokenizer.from_pretrained('firqaaa/indo-dpr-question_encoder-multiset-base')
model = DPRQuestionEncoder.from_pretrained('firqaaa/indo-dpr-question_encoder-multiset-base')
input_ids = tokenizer("Siapakah tokoh antagonis terkuat dalam serial DragonBall Super?", 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-question_encoder-multiset-base",
passage_embedding_model="firqaaa/indo-dpr-question_encoder-multiset-base",
max_seq_len_query=64,
max_seq_len_passage=256,
batch_size=16,
use_gpu=True,
embed_title=True,
use_fast_tokenizers=True)