This is a library for training and applying sparse fine-tunings with torch and transformers.

Related tags

Deep Learning composable-sft

Overview

This is a library for training and applying sparse fine-tunings with torch and transformers. Please refer to our paper Composable Sparse Fine-Tuning for Cross Lingual Transfer for background.

Installation

First, install Python 3.9 and PyTorch >= 1.9 (earlier versions may work but haven't been tested), e.g. using conda:

conda create -n sft python=3.9
conda activate sft
conda install pytorch cudatoolkit=11.1 -c pytorch -c conda-forge

Then download and install composable-sft:

git clone https://github.com/cambridgeltl/composable-sft.git
cd composable-sft
pip install -e .

Using pre-trained SFTs

Pre-trained SFTs can be downloaded directly and applied to models as follows:

from transformers import AutoConfig, AutoModelForTokenClassification
from sft import SFT

config = AutoConfig.from_pretrained(
    'bert-base-multilingual-cased',
    num_labels=17,
)

model = AutoModelForTokenClassification.from_pretrained(
    'bert-base-multilingual-cased',
    config=config,
)

language_sft = SFT('cambridgeltl/mbert-lang-sft-bxr-small') # SFT for Buryat
task_sft = SFT('cambridgeltl/mbert-task-sft-pos') # SFT for POS tagging

# Apply SFTs to pre-trained mBERT TokenClassification model
language_sft.apply(model)
task_sft.apply(model)

For a full list of pre-trained SFTs available, see MODELS

Example Scripts

Example scripts are provided in examples/ to show how to train SFTs using LT-SFT and evaluate them.

Citation

If you use this software, please cite the following paper:

@misc{ansell2021composable,
      title={Composable Sparse Fine-Tuning for Cross-Lingual Transfer},
      author={Alan Ansell and Edoardo Maria Ponti and Anna Korhonen and Ivan Vuli\'{c}},
      year={2021},
      eprint={2110.07560},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

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Comments

Adapters models

Hi,

Really nice work. Everything seems to work fine.

I'm wondering if you plan to release the Adapters models (based on MAD-X) too for comparison. I understand that this is focused on SFT, it would be nice to also have the Adapters.

opened by dadelani 2
[Q] LT-SFT and enc-dec models?

I'm wondering if this method should (theoretically) work with enc-dec models? Have You tried to train those models with code from this repository? I'm interested in utilizing this approach with T5 model.

opened by adamwawrzynski 3

This is a library for training and applying sparse fine-tunings with torch and transformers.

Related tags

Overview

Installation

Using pre-trained SFTs

Example Scripts

Citation

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Comments

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[Q] LT-SFT and enc-dec models?

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