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PLEASE SEE UPDATES IN SECTION 1.6
Introduction
Language understanding is an ongoing challenge and one of the most relevant and influential areas across any industry.
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root@tessa002:/workspace/nvidia-examples/bert/data# mkdir download root@tessa002:/workspace/nvidia-examples/bert/data# cd download root@tessa002:/workspace/nvidia-examples/bert/data/download# mkdir -p download/google_pretrained_weights root@tessa002:/workspace/nvidia-examples/bert/data/download# cd download/google_pretrained_weights/ root@tessa002:/workspace/nvidia-examples/bert/data/download/google_pretrained_weights# wget https://storage.googleapis.com/bert_models/2018_10_18/uncased_L-12_H-768_A-12.zip root@tessa002:/workspace/nvidia-examples/bert/data/download/google_pretrained_weights# unzip uncased_L-12_H-768_A-12.zip Archive: uncased_L-12_H-768_A-12.zip creating: uncased_L-12_H-768_A-12/ inflating: uncased_L-12_H-768_A-12/bert_model.ckpt.meta inflating: uncased_L-12_H-768_A-12/bert_model.ckpt.data-00000-of-00001 inflating: uncased_L-12_H-768_A-12/vocab.txt inflating: uncased_L-12_H-768_A-12/bert_model.ckpt.index inflating: uncased_L-12_H-768_A-12/bert_config.json |
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root@tessa002:/workspace/nvidia-examples/bert/data/download# mkdir -p squad/v1.1 root@tessa002:/workspace/nvidia-examples/bert/data/download# cd squad/v1.1 root@tessa002:/workspace/nvidia-examples/bert/data/download/squad# mkdir squad/v1.1# wget https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v1.1.json root@tessa002:/workspace/nvidia-examples/bert/data/download/squad# cd v1.1/ root@tessa002:/workspace/nvidia-examples/bert/data/download/squad/v1.1# wget https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v1.1.json root@tessa002:/workspace/nvidia-examples/bert/data/download/squad/v1.1# wget https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v1.1.json root@tessa002:/workspace/nvidia-examples/bert/data/download/squad/v1.1# wget https://github.com/allenai/bi-att-flow/archive/master.zip root@tessa002:/workspace/nvidia-examples/bert/data/download/squad/v1.1# unzip master.zip root@tessa002:/workspace/nvidia-examples/bert/data/download/squad/v1.1# cdcp bi-att-flow-master/ root@tessa002:/workspace/nvidia-examples/bert/data/download/squad/v1.1/bi-att-flow-master# cd squad root@tessa002:/workspace/nvidia-examples/bert/data/download/squad/v1.1/bi-att-flow-master/squad# cp evaluate-v1.1.py /workspace/nvidia-examples/bert/data/download/squad/v1.1/squad/evaluate-v1.1.py . root@tessa002:cd /workspace/nvidia-examples/bert |
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Note : consider logging results with “>2&1 | tee $LOGFILE” for submissions to judges
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root@tessa002:/workspace# mkdir lambdal root@tessa002:/workspace# cd lambdal root@tessa002:/workspace/lambdal# git clone https://github.com/lambdal/bert root@tessa002:/workspace/lambdal/lambdal# cd bert root@tessa002:/workspace/lambdal/bert# mpirun -np 4 -H localhost:4 -bind-to none -map-by slot -x NCCL_DEBUG=INFO -x LD_LIBRARY_PATH -x PATH -mca pml ob1 -mca btl ^openib --allow-run-as-root python3 run_squad_hvd.py --vocab_file=/workspace/nvidia-examples/bert/data/download/google_pretrained_weights/uncased_L-12_H-768_A-12/vocab.txt --bert_config_file=/workspace/nvidia-examples/bert/data/download/google_pretrained_weights/uncased_L-12_H-768_A-12/bert_config.json --init_checkpoint=/workspace/nvidia-examples/bert/data/download/google_pretrained_weights/uncased_L-12_H-768_A-12/bert_model.ckpt --do_train=True --train_file=/workspace/nvidia-examples/bert/data/download/squad/v1.1/train-v1.1.json --do_predict=True --predict_file=/workspace/nvidia-examples/bert/data/download/squad/v1.1/dev-v1.1.json --train_batch_size=12 --learning_rate=3e-5 --num_train_epochs=2.0 --max_seq_length=384 --doc_stride=128 --output_dir=/results/lambdal/squad1/squad_base/ --horovod=true |
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Must stick to pre-defined model (BERT-Base, Uncased)
Teams can locally cache (on SSD) starting model weights and dataset
HuggingFace implementation (TensorFlow/PyTorch) is the official standard. Usage of other implementation, or modification to official, is subject to approval.
Teams are allowed to explore different optimizers (SGD/Adam etc.) or learning rate schedules, or any other techniques that do not modify model architecture.Teams are not allowed to modify any model hyperparameters or add additional layers.
Entire model must be fine-tuned (cannot freeze layers)
You must provide all scripts and methodology used to achieve results
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Training scripts with their full training routine and command lines and output
Evaluation-only script for verification of result. Final evaluation is on a fixed sequence length (128 tokens).
Final model ckpt and inference files
Team’s training scripts and methodology, command line and logs of runs
run_squad.py predictions.json and nbest_predictions.json
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Final scores from unseen data of multiple questions; prediction from file, using standard run_squad.py
1.6 UPDATES (June 8, 2020)
In past discussion we had questions on training BERT from scratch; this is beyond the scope of this competition and is not allowed. You will need to use the BERT-BASE model file as outlined in the guidelines section 1.2.3
Change/modify the output layer and to allow additional layers
Allow for ensemble techniques
We must disallow integration of dev-set data into training dataset ; the SQUAD 1.1 datasets must remain unchanged / augmented
We must disallow additional external data integrated into training dataset for this competition because there is not enough time to be able to verify that the dev-set data might inadvertently be part of that acquired dataset augmentation
We allow any hyper-parameters ; ie. learn rate, optimizer, drop-out, etc.
We will also allow setting for random seed. This will reduce the variance between training runs
The F1 score will be used as score for team ranking.
Teams should submit their best 5 runs, please upload your runs in separate folders containing ckpt, logs, etc. - you/we will average top 3 of the 5 f1 scores for your final score
We will use the F1 as the quality metric for score / ranking. We will not round the output score computed from the output of the evaluate-v1.1.py.
The judges will score with standard evaluate-v1.1.py from Squad 1.1 as outlined in section 1.5 of the SQuAD 1.1 with Tensorflow BERT-BASE Guidelines.
We will use the probability score for unseen inference data (as test_input.json) to be provided no later than June 10th, as a secondary ranking in the event of any tie to the f1 average scoring against your top training run.