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question-answering mask_token: [MASK]
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								$
								curl -X POST \
-H "Authorization: Bearer YOUR_ORG_OR_USER_API_TOKEN" \
-H "Content-Type: application/json" \
-d '{"question": "Where does she live?", "context": "She lives in Berlin."}' \
https://api-inference.huggingface.co/models/wptoux/albert-chinese-large-qa
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wptoux/albert-chinese-large-qa wptoux/albert-chinese-large-qa
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pytorch

tf

Contributed by

wptoux wptoux
1 model

How to use this model directly from the 馃/transformers library:

			
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from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("wptoux/albert-chinese-large-qa") model = AutoModelForQuestionAnswering.from_pretrained("wptoux/albert-chinese-large-qa")

albert-chinese-large-qa

Albert large QA model pretrained from baidu webqa and baidu dureader datasets.

Data source

  • baidu webqa 1.0
  • baidu dureader

Traing Method

We combined the two datasets together and created a new dataset in squad format, including 705139 samples for training and 69638 samples for validation. We finetune the model based on the albert chinese large model.

Hyperparams

  • learning_rate 1e-5
  • max_seq_length 512
  • max_query_length 50
  • max_answer_length 300
  • doc_stride 256
  • num_train_epochs 2
  • warmup_steps 1000
  • per_gpu_train_batch_size 8
  • gradient_accumulation_steps 3
  • n_gpu 2 (Nvidia Tesla P100)

Usage

from transformers import AutoModelForQuestionAnswering, BertTokenizer

model = AutoModelForQuestionAnswering.from_pretrained('wptoux/albert-chinese-large-qa')
tokenizer = BertTokenizer.from_pretrained('wptoux/albert-chinese-large-qa')

Important: use BertTokenizer

MoreInfo

Please visit https://github.com/wptoux/albert-chinese-large-webqa for details.