Competition : Biocreative VII Official Website
Team : Biobot
Track 3: Automatic extraction of medication names in tweets
- Build corpora from SMM4H18 trainset
- Make evaluation code ready
- Make src ready from notebook
- add gpu running feature
- Put it on grid
- Add inference code
- Add testing code
- Run initial baseline char-rnn - only biotrainset
- Birnn- attention-char
- Create word-rnn model
- Testing word model working
- Rnn- word
- Bi-rnn -word
- Bi-rnn attention -word
- Bert /scibert ---> to be resolved
- resolved bert inference
- word-char model
- Crf
- Custom loss w/ best char model
- Custom loss w/ best word model
- Use different/custom losses
- Think augmenation
- Add requirements.txt
Clone the repository : git clone https://github.com/amansinha09/bio-challenge.git
Create a virtual environment (venv) with requirements.txt
using the commans below:
python3 -m venv .venv
. .venv/bin/activate
pip3 install --upgrade pip
cd bio-challenge
pip3 install -r requirements.txt
bio-challenge/
|-- data/
|-- src/
|-- data.py
[-- utils.py
|-- evaluation.py
|-- model.py
|-- run.py
|-- ref/
|-- res/
|-- .logs/
|-- .model/
|--run_exp.sh
|-- README.md
mkdir ref/ res/ .model/ .logs/
cd .logs/
sh ../run_exp.sh
Change the parameters for custom experiments :
usage: run.py [-h] [--device DEVICE] [--hs HS] [--epochs EPOCHS] [--bs BS]
[--nl NL] [--bidir] [--inplen INPLEN] [--inpsize INPSIZE]
[--vocabsize VOCABSIZE] [--lr LR] [--test_every TEST_EVERY]
[--save_dir SAVE_DIR] [--model_id MODEL_ID] [--save_preds]
[--save_model] [--stop_early]
Running ner...
optional arguments:
-h, --help show this help message and exit
--device DEVICE cpu or gpu
--hs HS Hidden layer size
--epochs EPOCHS Number of epochs
--bs BS batch size
--nl NL Number of layers
--bidir bi-directional
--inplen INPLEN sequence/sentence length
--inpsize INPSIZE embedding size
--vocabsize VOCABSIZE
vocab size
--lr LR Learning rate of loss optimization
--test_every TEST_EVERY
Testing after steps
--save_dir SAVE_DIR Model dir
--model_id MODEL_ID model name identifier
--save_preds whether to save the preditions
--save_model whether to save the model
--stop_early whether to use early stopping
Add the prediction file into the res/ folder and test file in the ref/ folder, then run the evalution script: python src/evaluation.py ref/<gold_file> res/<pred_file>