
# 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.

{"language": ["zh"], "license": "apache-2.0", "tags": ["Question Answering"], "datasets": ["webqa", "dureader"]}
transformers pytorch albert question-answering Question Answering zh dataset:webqa dataset:dureader license:apache-2.0 endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #albert #question-answering #Question Answering #zh #dataset-webqa #dataset-dureader #license-apache-2.0 #endpoints_compatible #has_space #region-us 


# 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

*Important: use BertTokenizer*

## MoreInfo
Please visit URL for details.

# 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

*Important: use BertTokenizer* ## MoreInfo
Please visit URL for details.
TAGS
#transformers #pytorch #albert #question-answering #Question Answering #zh #dataset-webqa #dataset-dureader #license-apache-2.0 #endpoints_compatible #has_space #region-us 
 # 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

*Important: use BertTokenizer* ## MoreInfo
Please visit URL for details.
passage: TAGS
#transformers #pytorch #albert #question-answering #Question Answering #zh #dataset-webqa #dataset-dureader #license-apache-2.0 #endpoints_compatible #has_space #region-us 
# 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

*Important: use BertTokenizer*## MoreInfo
Please visit URL for details.