deepspeedai / deepspeedai/DeepSpeedExamples
BingBertSQuAD Fine-tuning result mismatch tutorial document
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 6.8k
- Forks
- 1.1k
- Avg merge
- 2d 16h
- Merged PRs (30d)
- 1
Description
I rerun the shell file run_squad_baseline.sh under BingBertSquad without any modification on 8 gpus V100, the pretrain mode is https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-whole-word-masking-pytorch_model.bin, but I didn't get right results.
The document says for default config, the result should be EM: 87.27, F1: 93.33, but I got {"exact_match": 8.136234626300851, "f1": 16.67697307405455}.
and for PER_GPU_BATCH_SIZE=3, traing speed is 8.31it/s, means samples/second is about 24.93, which is aroud 36.34 in document.
Did I miss something?
Contributor guide
No contributing guide indexed for this repository
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reviewing run_squad_baseline.sh and the linked BERT fine-tuning tutorial, then reproduce the reported run on 8 V100 GPUs with the stated pretrained model. Compare the default metrics and the PER_GPU_BATCH_SIZE=3 throughput with the documented values; done means identifying and documenting the cause of the mismatch or confirming the tutorial needs correction.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, shell
- Domain
- machine-learning
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100