Multilingual Albert Base Cased 64k

🚀 mALBERT Base Cased 64k

A pretrained multilingual language model using a masked language modeling (MLM) objective. It can capture bidirectional context and is suitable for various downstream tasks.

🚀 Quick Start

You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to be fine-tuned on a downstream task. See the model hub to look for fine-tuned versions on a task that interests you.

💻 Usage Examples

Basic Usage

Here is how to use this model to get the features of a given text in PyTorch:

from transformers import AlbertTokenizer, AlbertModel
tokenizer = AlbertTokenizer.from_pretrained('cservan/multilingual-albert-base-cased-64k')
model = AlbertModel.from_pretrained("cservan/multilingual-albert-base-cased-64k")
text = "Remplacez-moi par le texte en français que vous souhaitez."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)

And in TensorFlow:

from transformers import AlbertTokenizer, TFAlbertModel
tokenizer = AlbertTokenizer.from_pretrained('cservan/multilingual-albert-base-cased-64k')
model = TFAlbertModel.from_pretrained("cservan/multilingual-albert-base-cased-64k")
text = "Remplacez-moi par le texte en français que vous souhaitez."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)

✨ Features

• Multilingual Support: Supports multiple languages including French, English, German, Spanish, etc.
• Bidirectional Representation: Learns a bidirectional representation of the sentence through Masked Language Modeling (MLM).
• Shared Layers: Shares its layers across its Transformer, resulting in a small memory footprint.

📦 Installation

No specific installation steps are provided in the original document.

📚 Documentation

Model description

mALBERT is a transformers model pretrained on 16Go of French Wikipedia in a self - supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was pretrained with two objectives:

• Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the sentence.
• Sentence Ordering Prediction (SOP): mALBERT uses a pretraining loss based on predicting the ordering of two consecutive segments of text.

This way, the model learns an inner representation of the languages that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the mALBERT model as inputs.

mALBERT is particular in that it shares its layers across its Transformer. Therefore, all layers have the same weights. Using repeating layers results in a small memory footprint, however, the computational cost remains similar to a BERT - like architecture with the same number of hidden layers as it has to iterate through the same number of (repeating) layers.

This is the second version of the base model.

This model has the following configuration:

Intended uses & limitations

This model is primarily aimed at being fine - tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2.

Training data

The mALBERT model was pretrained on 13go of Multiligual Wikipedia (excluding lists, tables and headers).

Training procedure

Preprocessing

The texts are lowercased and tokenized using SentencePiece and a vocabulary size of 128,000. The inputs of the model are then of the form:

[CLS] Sentence A [SEP] Sentence B [SEP]

Training

The mALBERT procedure follows the BERT setup.

The details of the masking procedure for each sentence are the following:

• 15% of the tokens are masked.
• In 80% of the cases, the masked tokens are replaced by [MASK].
• In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.
• In the 10% remaining cases, the masked tokens are left as is.

Tools

The tools used to pre - train the model are available [here](https://gitlab.lisn.upsaclay.fr/nlp/deep - learning/UER - py)

Evaluation results

When fine - tuned on downstream tasks, the ALBERT models achieve the following results:

Slot - filling

Classification task

🔧 Technical Details

The model uses self - supervised learning on raw text data. The Masked Language Modeling (MLM) objective allows it to learn bidirectional context, and the Sentence Ordering Prediction (SOP) helps it understand the ordering of text segments. The shared layers in the Transformer architecture reduce the memory footprint while maintaining a similar computational cost to a BERT - like architecture with the same number of hidden layers.

📄 License

This project is licensed under the Apache - 2.0 license.

BibTeX entry and citation info

@inproceedings{servan2024mALBERT,
  author    = {Christophe Servan and
               Sahar Ghannay and
               Sophie Rosset},
  booktitle = {the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC - COLING 2024)},
  title     = {{mALBERT: Is a Compact Multilingual BERT Model Still Worth It?}},
  year      = {2024},
  address   = {Torino, Italy},
  month     = may,
}

Link to the paper: [PDF](https://hal.science/hal - 04520797)