- BERT is used for sequence annotation and text categorization template code to facilitate BERT for more tasks. Welcome to use this BERT template to solve more NLP tasks, and then share your results and code here.
- 这是使用BERT进行序列注释和文本分类的模板代码，方便大家将BERT用于更多任务。欢迎使用这个BERT模板解决更多NLP任务，然后在这里分享你的结果和代码。
- 项目例子具体使用方法见 BERT-for-Sequence-Labeling-and-Text-Classification的使用方法.ipynb
- Move google's BERT code to file
bert(I've prepared a copy for you.); - Download google's BERT pretrained model and unzip then to file
pretrained_model; - Run Code! You can change task_name and output_dir.
python run_text_classification.py \
--task_name=Snips \
--do_train=true \
--do_eval=true \
--data_dir=data/snips_Intent_Detection_and_Slot_Filling \
--vocab_file=pretrained_model/uncased_L-12_H-768_A-12/vocab.txt \
--bert_config_file=pretrained_model/uncased_L-12_H-768_A-12/bert_config.json \
--init_checkpoint=pretrained_model/uncased_L-12_H-768_A-12/bert_model.ckpt \
--max_seq_length=128 \
--train_batch_size=32 \
--learning_rate=2e-5 \
--num_train_epochs=3.0 \
--output_dir=./output/snips_Intent_Detection/
| name | function |
|---|---|
| bert | store google's BERT code |
| data | store task data set |
| output | store model output |
| pretrained_model | store BERT pretrained model |
| run_sequence_labeling.py | for Sequence Labeling Task |
| run_text_classification.py | for Text Classification Task |
| run_sequence_labeling_and_text_classification.py | for join task (come soon!) |
| calculate_model_score.py | for evaluation model |
Just write a small piece of code according to the existing template!
For example, If you have a new classification task QQP.
Before running this example you must download the GLUE data by running this script.
Now, write code!
class QqpProcessor(DataProcessor):
"""Processor for the QQP data set."""
def get_train_examples(self, data_dir):
"""See base class."""
return self._create_examples(
self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
def get_dev_examples(self, data_dir):
"""See base class."""
return self._create_examples(
self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
def get_test_examples(self, data_dir):
"""See base class."""
return self._create_examples(
self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
def get_labels(self):
"""See base class."""
return ["0", "1"]
def _create_examples(self, lines, set_type):
"""Creates examples for the training and dev sets."""
examples = []
for (i, line) in enumerate(lines):
if i == 0 or len(line)!=6:
continue
guid = "%s-%s" % (set_type, i)
text_a = tokenization.convert_to_unicode(line[3])
text_b = tokenization.convert_to_unicode(line[4])
if set_type == "test":
label = "1"
else:
label = tokenization.convert_to_unicode(line[5])
examples.append(
InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
return examples
Registration task
def main(_):
tf.logging.set_verbosity(tf.logging.INFO)
processors = {
"qqp": QqpProcessor,
}
python run_text_classification.py \
--task_name=qqp \
--do_train=true \
--do_eval=true \
--data_dir=data/snips_Intent_Detection_and_Slot_Filling \
--vocab_file=pretrained_model/uncased_L-12_H-768_A-12/vocab.txt \
--bert_config_file=pretrained_model/uncased_L-12_H-768_A-12/bert_config.json \
--init_checkpoint=pretrained_model/uncased_L-12_H-768_A-12/bert_model.ckpt \
--max_seq_length=128 \
--train_batch_size=32 \
--learning_rate=2e-5 \
--num_train_epochs=3.0 \
--output_dir=./output/qqp_Intent_Detection/
Welcome to add!
| Task name | Explain | data source |
|---|---|---|
| CoNLL-2003 named entity recognition | NER | |
| Atis Joint Slot Filling and Intent Prediction | https://github.com/MiuLab/SlotGated-SLU/tree/master/data/atis | |
| Snips Joint Slot Filling and Intent Prediction | https://github.com/MiuLab/SlotGated-SLU/tree/master/data/snips | |
| GLUE |
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent