This model is a
RoBERTa base model pre-trained on the
FreEMmax corpus for Early Modern French. It was
introduced in
this paper. This model is Cased and was trained with a mix of normalized and unnormalized data.
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:
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.
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.
D'AlemBERT was pretrained on the non-freely available version of the
FreEMmax corpus, 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.
1@inproceedings{gabay-etal-2022-freem,
2 title = "From {F}re{EM} to D{'}{A}lem{BERT}: a Large Corpus and a Language Model for Early {M}odern {F}rench",
3 author = "Gabay, Simon and
4 Ortiz Suarez, Pedro and
5 Bartz, Alexandre and
6 Chagu{\'e}, Alix and
7 Bawden, Rachel and
8 Gambette, Philippe and
9 Sagot, Beno{\^\i}t",
10 booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
11 month = jun,
12 year = "2022",
13 address = "Marseille, France",
14 publisher = "European Language Resources Association",
15 url = "https://aclanthology.org/2022.lrec-1.359",
16 pages = "3367--3374",
17 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.",
18}
