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README.md

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+---
+language: fr
+license: apache-2.0
+datasets:
+- wikipedia
+---
+
+# frALBERT Base
+
+Pretrained model on French language using a masked language modeling (MLM) objective. It was introduced in
+[this paper](https://arxiv.org/abs/1909.11942) and first released in
+[this repository](https://github.com/google-research/albert). This model, as all ALBERT models, is uncased: it does not make a difference
+between french and French.
+
+## Model description
+
+frALBERT is a transformers model pretrained on 4Go 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): frALBERT 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 English language 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 frALBERT model as inputs.
+
+frALBERT 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 first version of the base model. 
+
+This model has the following configuration:
+
+- 12 repeating layers
+- 128 embedding dimension
+- 768 hidden dimension
+- 12 attention heads
+- 11M parameters
+
+## Intended uses & limitations
+
+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](https://huggingface.co/models?filter=fralbert) to look for
+fine-tuned versions on a task that interests you.
+
+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 GPT2.
+
+### How to use
+
+You can use this model directly with a pipeline for masked language modeling:
+
+```python
+>>> from transformers import pipeline
+>>> unmasker = pipeline('fill-mask', model='fralbert-base')
+>>> unmasker("Bonjour Je suis un model [MASK] .")
+```
+
+Here is how to use this model to get the features of a given text in PyTorch:
+
+```python
+from transformers import AlbertTokenizer, AlbertModel
+tokenizer = AlbertTokenizer.from_pretrained('fralbert-base')
+model = AlbertModel.from_pretrained("fralbert-base")
+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:
+
+```python
+from transformers import AlbertTokenizer, TFAlbertModel
+tokenizer = AlbertTokenizer.from_pretrained('fralbert-base')
+model = TFAlbertModel.from_pretrained("fralbert-base")
+text = "Remplacez-moi par le texte en français que vous souhaitez."
+encoded_input = tokenizer(text, return_tensors='tf')
+output = model(encoded_input)
+```
+
+
+## Training data
+
+The frALBERT model was pretrained on 4go of [French Wikipedia](https://fr.wikipedia.org/wiki/French_Wikipedia) (excluding lists, tables and
+headers).
+
+## Training procedure
+
+### Preprocessing
+
+The texts are lowercased and tokenized using SentencePiece and a vocabulary size of 32,000. The inputs of the model are
+then of the form:
+
+```
+[CLS] Sentence A [SEP] Sentence B [SEP]
+```
+
+### Training
+
+The frALBERT 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.
+
+## Evaluation results
+
+When fine-tuned on downstream tasks, the ALBERT models achieve the following results:
+
+|                | FQuAD1.0 | PIAF_dev 
+|----------------|----------|----------
+|frALBERT-base   |72.6/55.1 |61.0 / 38.9 
+
+
+### BibTeX entry and citation info
+
+```bibtex
+@inproceedings{cattan2021fralbert,
+  author    = {Oralie Cattan and
+               Christophe Servan and
+               Sophie Rosset},
+  booktitle = {Recent Advances in Natural Language Processing, RANLP 2021},
+  title     = {{On the Usability of Transformers-based models for a French Question-Answering task}},
+  year      = {2021},
+  address   = {Online},
+  month     = sep,
+}
+```