Multilingual Albert Base Cased 32k

🚀 mALBERT Base Cased 32k

A pretrained multilingual language model using a masked language modeling (MLM) objective. It can distinguish between words like "french" and "French".

🚀 Quick Start

This is a pretrained multilingual language model. 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

from transformers import AlbertTokenizer, AlbertModel
tokenizer = AlbertTokenizer.from_pretrained('cservan/multilingual-albert-base-cased-32k')
model = AlbertModel.from_pretrained("cservan/multilingual-albert-base-cased-32k")
text = "Remplacez-moi par le texte en français que vous souhaitez."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)

Advanced Usage

from transformers import AlbertTokenizer, TFAlbertModel
tokenizer = AlbertTokenizer.from_pretrained('cservan/multilingual-albert-base-cased-32k')
model = TFAlbertModel.from_pretrained("cservan/multilingual-albert-base-cased-32k")
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.
• Cased Model: Can distinguish between uppercase and lowercase words, e.g., "french" and "French".
• Self - Supervised Learning: Pretrained on a large amount of French Wikipedia data in a self - supervised fashion.
• Two Pretraining Objectives: Masked language modeling (MLM) and Sentence Ordering Prediction (SOP).
• Shared Layers: Shares its layers across its Transformer, resulting in a small memory footprint.

📦 Installation

The installation process depends on the specific usage scenarios. You need to install the transformers library. You can use the following command to install it:

pip install transformers

📚 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:

🔧 Technical Details

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:

📄 License

This model 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)