Typo corrections

README.md

@@ -5,7 +5,7 @@ datasets:
 - wikipedia
 ---
 
-# frALBERT Base
+# 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
@@ -14,7 +14,7 @@ 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
+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:
@@ -24,13 +24,13 @@ was pretrained with two objectives:
   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.
+- 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.
+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.
+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. 
 
@@ -87,7 +87,7 @@ 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
+The FrALBERT model was pretrained on 4go of [French Wikipedia](https://fr.wikipedia.org/wiki/French_Wikipedia) (excluding lists, tables and
 headers).
 
 ## Training procedure
@@ -103,7 +103,7 @@ then of the form:
 
 ### Training
 
-The frALBERT procedure follows the BERT setup.
+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.