A framework for materials science NER using the HuggingFace Transformers NLP Toolkit.
git clone https://github.com/walkernr/MatBERT_NER.git MatBERT_NER
cd MatBERT_NER
pip install -r requirements.txt .
Download the model weights here
The folowing command will train the MatBERT model on the solid state dataset using default parameters
python train.py -dv gpu:0 -ds solid_state -ml matbert
Additional parameters can be specified.
usage: train.py [-h] [-dv DEVICE] [-sd SEEDS] [-ts TAG_SCHEMES] [-st SPLITS] [-ds DATASETS] [-ml MODELS] [-sl] [-bs BATCH_SIZE] [-on OPTIMIZER_NAME] [-wd WEIGHT_DECAY] [-ne N_EPOCH]
[-eu EMBEDDING_UNFREEZE] [-tu TRANSFORMER_UNFREEZE] [-el EMBEDDING_LEARNING_RATE] [-tl TRANSFORMER_LEARNING_RATE] [-cl CLASSIFIER_LEARNING_RATE] [-sf SCHEDULING_FUNCTION]
[-km]
optional arguments:
-h, --help show this help message and exit
-dv DEVICE, --device DEVICE
computation device for model (e.g. cpu, gpu:0, gpu:1)
-sd SEEDS, --seeds SEEDS
comma-separated seeds for data shuffling and model initialization (e.g. 1,2,3 or 2,4,8)
-ts TAG_SCHEMES, --tag_schemes TAG_SCHEMES
comma-separated tagging schemes to be considered (e.g. iob1,iob2,iobes)
-st SPLITS, --splits SPLITS
comma-separated training splits to be considered, in percent (e.g. 80). test split will always be 10% and the validation split will be 1/8 of the training split
unless the training split is 100%
-ds DATASETS, --datasets DATASETS
comma-separated datasets to be considered (e.g. solid_state,doping)
-ml MODELS, --models MODELS
comma-separated models to be considered (e.g. matbert,scibert,bert)
-sl, --sentence_level
switch for sentence-level learning instead of paragraph-level
-bs BATCH_SIZE, --batch_size BATCH_SIZE
number of samples in each batch
-on OPTIMIZER_NAME, --optimizer_name OPTIMIZER_NAME
name of optimizer, add "_lookahead" to implement lookahead on top of optimizer (not recommended for ranger or rangerlars)
-wd WEIGHT_DECAY, --weight_decay WEIGHT_DECAY
weight decay for optimizer (excluding bias, gamma, and beta)
-ne N_EPOCH, --n_epoch N_EPOCH
number of training epochs
-eu EMBEDDING_UNFREEZE, --embedding_unfreeze EMBEDDING_UNFREEZE
epoch (index) at which bert embeddings are unfrozen
-tu TRANSFORMER_UNFREEZE, --transformer_unfreeze TRANSFORMER_UNFREEZE
comma-separated number of transformers (encoders) to unfreeze at each epoch
-el EMBEDDING_LEARNING_RATE, --embedding_learning_rate EMBEDDING_LEARNING_RATE
embedding learning rate
-tl TRANSFORMER_LEARNING_RATE, --transformer_learning_rate TRANSFORMER_LEARNING_RATE
transformer learning rate
-cl CLASSIFIER_LEARNING_RATE, --classifier_learning_rate CLASSIFIER_LEARNING_RATE
pooler/classifier learning rate
-sf SCHEDULING_FUNCTION, --scheduling_function SCHEDULING_FUNCTION
function for learning rate scheduler (linear, exponential, or cosine)
-km, --keep_model switch for saving the best model parameters to disk
To train on custom annotated datasets, the train.py script has a dictionary data_files where additional datasets can be specified. Similarly, alternative pre-trained models can be used by modifying the model_files dictionary.
For prediction, the predict function contained within predict.py can be used. An example that was used internally can be found in the predict_script.py file. Furthermore, an example utilizing MongoDB can be found in the predict_mongo.py script. Note that these two examples will need to be edited for your specific needs to be usable.
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