README.md

---
language: fr
tags:
- Early Modern French
- Historical
license: apache-2.0
datasets:
- freemmax
---

<a href="https://portizs.eu/publication/2022/lrec/dalembert/">
	<img width="300px" src="https://portizs.eu/publication/2022/lrec/dalembert/featured_hu18bf34d40cdc71c744bdd15e48ff0b23_61788_720x2500_fit_q100_h2_lanczos_3.webp">
</a>
# D'AlemBERT base model

This model is a [RoBERTa base model](https://huggingface.co/bert-base-uncased) pre-trained on the [FreEMmax corpus](https://doi.org/10.5281/zenodo.6481135) for Early Modern French. It was
introduced in [this paper](https://aclanthology.org/2022.lrec-1.359/). This model is Cased and was trained with a mix of normalized and unnormalized data.

## Model description

D'AlemBERT is a transformers mode pretrained on the raw texts only with no humans labelling them in any way with an automatic process to generate inputs and labels from those texts using the RoBERTa base model. More precisely, it was pretrained
with one objective:

- Masked language modeling (MLM): this is part of the original training loss of the BERT base model. When 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.

## Intended uses & limitations

You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task.

Note that 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 GPT.

The model is primarily intended for use in Digital Humanities and Historical NLP.

### Limitations and bias

This model is trained with historical French data from starting from the 16th c., so it might produce results that seem extremely biased by today standards. It might not work well on contemporary data and it is not intended to be used on it.

This bias will also affect all fine-tuned versions of this model.

## Training data

D'AlemBERT was pretrained on the non-freely available version of the [FreEMmax corpus](https://doi.org/10.5281/zenodo.6481135), a dataset
consisting of more than 180k tokens coming from 22 different sources, and comprising French textual data going from the 16th c to the early 20th c.

### BibTeX entry and citation info

```bibtex
@inproceedings{gabay-etal-2022-freem,
    title = "From {F}re{EM} to D{'}{A}lem{BERT}: a Large Corpus and a Language Model for Early {M}odern {F}rench",
    author = "Gabay, Simon  and
      Ortiz Suarez, Pedro  and
      Bartz, Alexandre  and
      Chagu{\'e}, Alix  and
      Bawden, Rachel  and
      Gambette, Philippe  and
      Sagot, Beno{\^\i}t",
    booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
    month = jun,
    year = "2022",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://aclanthology.org/2022.lrec-1.359",
    pages = "3367--3374",
    abstract = "anguage models for historical states of language are becoming increasingly important to allow the optimal digitisation and analysis of old textual sources. Because these historical states are at the same time more complex to process and more scarce in the corpora available, this paper presents recent efforts to overcome this difficult situation. These efforts include producing a corpus, creating the model, and evaluating it with an NLP task currently used by scholars in other ongoing projects.",
}
```