metadata
language: zh-tw
datasets: DRCD
tasks: Question Answering
BERT DRCD 384
This model is a fine-tune checkpoint of bert-base-chinese, fine-tuned on DRCD dataset. This model reaches a F1 score of 86. This model reaches a EM score of 83.
Training Arguments:
length: 384
stride: 128
learning_rate: 3e-5
batch_size: 10
epoch: 3
Deployment
Deploy BERT-DRCD-QuestionAnswering model using FastAPI and containerized using Docker.
Usage
In Transformers
text = "鴻海科技集團是由臺灣企業家郭台銘創辦的跨國企業，總部位於臺灣新北市土城區，主要生產地則在中國大陸，以富士康做為商標名稱。其專注於電子產品的代工服務，研發生產精密電氣元件、機殼、準系統、系統組裝、光通訊元件、液晶顯示件等3C產品上、下游產品及服務。"
query = "鴻海集團總部位於哪裡？"
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
tokenizer = BertTokenizerFast.from_pretrained("nyust-eb210/braslab-bert-drcd-384")
model = BertForQuestionAnswering.from_pretrained("nyust-eb210/braslab-bert-drcd-384").to(device)
encoded_input = tokenizer(text, query, return_tensors="pt").to(device)
qa_outputs = model(**encoded_input)
start = torch.argmax(qa_outputs.start_logits).item()
end = torch.argmax(qa_outputs.end_logits).item()
answer = encoded_input.input_ids.tolist()[0][start : end + 1]
answer = "".join(tokenizer.decode(answer).split())
start_prob = torch.max(torch.nn.Softmax(dim=-1)(qa_outputs.start_logits)).item()
end_prob = torch.max(torch.nn.Softmax(dim=-1)(qa_outputs.end_logits)).item()
confidence = (start_prob + end_prob) / 2
print(answer, confidence) # 臺灣新北市土城區, 0.92