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model_cards/wptoux/albert-chinese-large-qa/README.md

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+# 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.