PerformerDualEncoder - Work in Progress

In this paper a train funtion will be implemented which will be used to train a performer for language agnostic representations.

Install dependencies

pip install -r requirements.txt

Usage (A pretrained model has not been released yet but will be in the future)

from transformers import AutoTokenizer
from modelling_dual_encoder import DualEncoderPerformer, DualEncoder

tokenizer = AutoTokenizer.from_pretrained("roberta-large")
model = DualEncoderPerformer(num_tokens=tokenizer.vocab_size)
# with huggingface transformers
# cls = AutoModel.from_pretrained("roberta-large")
# model = DualEncoder(cls)
sentences1 = tokenizer(["Ich bin Andre", "Ich bin Andre"],
                       add_special_tokens=True, return_tensors="pt",
                       padding=True)
sentences2 = tokenizer(["I am Andre", "I need support"],
                       add_special_tokens=True, return_tensors="pt",
                       padding=True)
print(model.get_similarity(sentences1, sentences2))

This code should output something like the following:

tensor([0.6409, 0.4435])

Training

At first modify the training parameters in env.list. These environ vars will be used during training. In addition there is a ds_config.json. In this json you can modify training parameters like learning rate. For more information about the ds_config.json check out deepspeed https://www.deepspeed.ai/docs/config-json/ The env.list parameters will be prioritized

Run a training on docker

At first build the images by running

bash build_images.sh

then run the training using:

docker run -d --cpuset-cpus="0-17" --runtime=nvidia -it -p 6016:6016 -v /path/to/data/storage:/storage \ 
                                                                     -v /path/to/model_save_dir/results:/results \
                                                                     --env-file ./env.list \
                                                                     --name transformer \
                                                                      performer_job \

The training script will automatically download the OPUS-100 Dataset into the storage directory and start training