en | ch
Course Project
review the pytorch framework and sequence-labeling task
practice using the transformers of Huggingface
A train set, a test set and a validation set in the data file
-DOCSTART- -X- O O
-sentnce- -pos- -Chuck- -Entity-
-data # source data
-emb # BERT model files
-util
-dataTool.py # data interface
-model.py
-trainer.py # train and evaluate
config.py # parameters in the project
run.py
requirement.txt
EDA.ipynb # exploratory data analasis,
# which aims to confirm the hyper-params in the trials
For keeping the convenience and simplicity of experiments,
decouple the model into two units: encoder and tagger
model ==> encoder + tagger
In such a way, encoder extracts the context and linguistit features,
which will be received by tagger to output BIO tags.
chmod 755 deploy
./deploy
./gpu n # monitor the GPU (refresh every n seconds)
./run # start
| Model | Precision | Recall | F1 |
|---|---|---|---|
| Bert-CRF | 0.71 | 0.68 | 0.69 |
| Bert-softmax | - | - | - |
| Bert-BiLSTM-CRF | - | - | - |
| Bert-BiLSTM-softmax | - | - | - |
- cost sensitive learning or drop the few classes
- dropout to improve the generalization performance
- different backbone structures
- DDP training --> large GPU caches for a large batch_size
- more epochs --> schedule the learning rate dynamically while training
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