xmod.md

X-MOD

Overview

The X-MOD model was proposed in Lifting the Curse of Multilinguality by Pre-training Modular Transformers by Jonas Pfeiffer, Naman Goyal, Xi Lin, Xian Li, James Cross, Sebastian Riedel, and Mikel Artetxe. X-MOD extends multilingual masked language models like XLM-R to include language-specific modular components (language adapters) during pre-training. For fine-tuning, the language adapters in each transformer layer are frozen.

The abstract from the paper is the following:

Multilingual pre-trained models are known to suffer from the curse of multilinguality, which causes per-language performance to drop as they cover more languages. We address this issue by introducing language-specific modules, which allows us to grow the total capacity of the model, while keeping the total number of trainable parameters per language constant. In contrast with prior work that learns language-specific components post-hoc, we pre-train the modules of our Cross-lingual Modular (X-MOD) models from the start. Our experiments on natural language inference, named entity recognition and question answering show that our approach not only mitigates the negative interference between languages, but also enables positive transfer, resulting in improved monolingual and cross-lingual performance. Furthermore, our approach enables adding languages post-hoc with no measurable drop in performance, no longer limiting the model usage to the set of pre-trained languages.

This model was contributed by jvamvas. The original code can be found here and the original documentation is found here.

Usage tips

Tips:

• X-MOD is similar to XLM-R, but a difference is that the input language needs to be specified so that the correct language adapter can be activated.
• The main models – base and large – have adapters for 81 languages.

Adapter Usage

Input language

There are two ways to specify the input language:

1. By setting a default language before using the model:

from transformers import XmodModel

model = XmodModel.from_pretrained("facebook/xmod-base")
model.set_default_language("en_XX")

2. By explicitly passing the index of the language adapter for each sample:

import torch

input_ids = torch.tensor(
    [
        [0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2],
        [0, 1310, 49083, 443, 269, 71, 5486, 165, 60429, 660, 23, 2],
    ]
)
lang_ids = torch.LongTensor(
    [
        0,  # en_XX
        8,  # de_DE
    ]
)
output = model(input_ids, lang_ids=lang_ids)

Fine-tuning

The paper recommends that the embedding layer and the language adapters are frozen during fine-tuning. A method for doing this is provided:

model.freeze_embeddings_and_language_adapters()
# Fine-tune the model ...

Cross-lingual transfer

After fine-tuning, zero-shot cross-lingual transfer can be tested by activating the language adapter of the target language:

model.set_default_language("de_DE")
# Evaluate the model on German examples ...

Resources

• Text classification task guide
• Token classification task guide
• Question answering task guide
• Causal language modeling task guide
• Masked language modeling task guide
• Multiple choice task guide

XmodConfig

[[autodoc]] XmodConfig

XmodModel

[[autodoc]] XmodModel - forward

XmodForCausalLM

[[autodoc]] XmodForCausalLM - forward

XmodForMaskedLM

[[autodoc]] XmodForMaskedLM - forward

XmodForSequenceClassification

[[autodoc]] XmodForSequenceClassification - forward

XmodForMultipleChoice

[[autodoc]] XmodForMultipleChoice - forward

XmodForTokenClassification

[[autodoc]] XmodForTokenClassification - forward

XmodForQuestionAnswering

[[autodoc]] XmodForQuestionAnswering - forward