### indo-dpr-question_encoder-multiset-base
<p style="font-size:16px">Indonesian Dense Passage Retrieval trained on translated SQuADv2.0 and Natural Question dataset in DPR format.</p>


### 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 |


<p style="font-size:16px">Note: This report is for evaluation on the dev set, after 27288 batches.</p>

### Usage

```python
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)
```


{"language": ["id"], "license": "apache-2.0", "tags": ["feature-extraction", "transformers"], "datasets": ["squad_v2", "natural_questions"], "metrics": ["accuracy", "f1", "precision", "recall"], "pipeline_tag": "feature-extraction"}
transformers pytorch bert feature-extraction id dataset:squad_v2 dataset:natural_questions license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #feature-extraction #id #dataset-squad_v2 #dataset-natural_questions #license-apache-2.0 #endpoints_compatible #region-us 

### indo-dpr-question\_encoder-multiset-base


Indonesian Dense Passage Retrieval trained on translated SQuADv2.0 and Natural Question dataset in DPR format.


### Evaluation




Note: This report is for evaluation on the dev set, after 27288 batches.


### Usage


You can use it using 'haystack' as follows:



### indo-dpr-question\_encoder-multiset-base


Indonesian Dense Passage Retrieval trained on translated SQuADv2.0 and Natural Question dataset in DPR format. ### Evaluation




Note: This report is for evaluation on the dev set, after 27288 batches. ### Usage


You can use it using 'haystack' as follows:
TAGS
#transformers #pytorch #bert #feature-extraction #id #dataset-squad_v2 #dataset-natural_questions #license-apache-2.0 #endpoints_compatible #region-us 
 ### indo-dpr-question\_encoder-multiset-base


Indonesian Dense Passage Retrieval trained on translated SQuADv2.0 and Natural Question dataset in DPR format. ### Evaluation




Note: This report is for evaluation on the dev set, after 27288 batches. ### Usage


You can use it using 'haystack' as follows:
TAGS
#transformers #pytorch #bert #feature-extraction #id #dataset-squad_v2 #dataset-natural_questions #license-apache-2.0 #endpoints_compatible #region-us 
### indo-dpr-question\_encoder-multiset-base


Indonesian Dense Passage Retrieval trained on translated SQuADv2.0 and Natural Question dataset in DPR format.### Evaluation




Note: This report is for evaluation on the dev set, after 27288 batches.### Usage


You can use it using 'haystack' as follows: