fintuned the kykim/bert-kor-base model as a dense passage retrieval context encoder by KLUE dataset this link is experiment result. https://wandb.ai/thingsu/DenseRetrieval
Corpus : Korean Wikipedia Corpus
Trained Strategy :
- Pretrained Model : kykim/bert-kor-base
- Inverse Cloze Task : 16 Epoch, by korquad v 1.0, KLUE MRC dataset
- In-batch Negatives : 12 Epoch, by KLUE MRC dataset, random sampling between Sparse Retrieval(TF-IDF) top 100 passage per each query
I'm not confident about this model will work in other dataset or corpus. ''' from Transformers import AutoTokenizer, BertPreTrainedModel, BertModel
class BertEncoder(BertPreTrainedModel): def init(self, config): super(BertEncoder, self).init(config)
self.bert = BertModel(config)
self.init_weights()
def forward(self, input_ids, attention_mask=None, token_type_ids=None):
outputs = self.bert(input_ids, attention_mask, token_type_ids)
pooled_output = outputs[1]
return pooled_output
model_name = 'kykim/bert-kor-base' tokenizer = AutoTokenizer.from_pretrained(model_name)
q_encoder = BertEncoder.from_pretrained("thingsu/koDPR_question") p_encoder = BertEncoder.from_pretrained("thingsu/koDPR_context")
'''