Add README

README.md

@@ -1,3 +1,144 @@
 ---
-license: mit
+language: fr
+license: apache-2.0
+datasets:
+- wikipedia
 ---
+
+# mALBERT Base Cased 64k
+
+Pretrained multilingual language model 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, unlike other ALBERT models, is cased: it does make a difference between french and French.
+
+## 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:
+
+- 12 repeating layers
+- 128 embedding dimension
+- 768 hidden dimension
+- 12 attention heads
+- 11M parameters
+- 64k of vocabulary size
+
+## 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=malbert-base-cased-64k) 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
+
+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('cservan/malbert-base-cased-64k')
+model = AlbertModel.from_pretrained("cservan/malbert-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:
+
+```python
+from transformers import AlbertTokenizer, TFAlbertModel
+tokenizer = AlbertTokenizer.from_pretrained('cservan/malbert-base-cased-64k')
+model = TFAlbertModel.from_pretrained("cservan/malbert-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)
+```
+
+
+## Training data
+
+The mALBERT 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 64,000. 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.
+
+## Evaluation results
+
+When fine-tuned on downstream tasks, the ALBERT models achieve the following results:
+
+Slot-filling:
+
+|Models ⧹ Tasks |  MMNLU |  MultiATIS++ |  CoNLL2003 |  MultiCoNER |  SNIPS |  MEDIA |
+|---------------|--------------|--------------|--------------|--------------|--------------|--------------|
+|EnALBERT |  N/A |  N/A |  89.67 (0.34) |  42.36 (0.22) |  95.95 (0.13) |  N/A |
+|FrALBERT |  N/A |  N/A |  N/A |  N/A |  N/A |  81.76 (0.59)
+|mALBERT-128k |  65.81 (0.11) |  89.14 (0.15) |  88.27 (0.24) |  46.01 (0.18) |  91.60 (0.31) |  83.15 (0.38) |
+|mALBERT-64k  |  65.29 (0.14) |  88.88 (0.14) |  86.44 (0.37) |  44.70 (0.27) |  90.84 (0.47) |  82.30 (0.19) |
+|mALBERT-32k  |  64.83 (0.22) |  88.60 (0.27) |  84.96 (0.41) |  44.13 (0.39) |  89.89 (0.68) |  82.04 (0.28) |
+
+Classification task:
+
+|Models ⧹ Tasks | MMNLU | MultiATIS++ | SNIPS | SST2 |
+|---------------|--------------|--------------|--------------|--------------|
+|mALBERT-128k | 72.35 (0.09) | 90.58 (0.98) | 96.84 (0.49) | 34.66 (1.46) |
+|mALBERT-64k  | 71.26 (0.11) | 90.97 (0.70) | 96.53 (0.44) | 34.64 (1.02) |
+|mALBERT-32k  | 70.76 (0.11) | 90.55 (0.98) | 96.49 (0.45) | 34.18 (1.64) |
+
+### BibTeX entry and citation info
+
+```bibtex
+@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)