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README.md

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 # FlauBERT-Oral models: Using ASR-Generated Text for Spoken Language Modeling
 
-**FlauBERT-Oral** are French BERT models trained on a very large amount of automatically transcribed speech from 350,000 hours of diverse French TV shows. They were trained with the [**FlauBERT software**] (https://github.com/getalp/Flaubert) using the same parameters as the [flaubert-base-uncased](https://huggingface.co/flaubert/flaubert_base_uncased) model (12 layers, 12 attention heads, 768 dims, 137M parameters, uncased).
+**FlauBERT-Oral** are French BERT models trained on a very large amount of automatically transcribed speech from 350,000 hours of diverse French TV shows. They were trained with the [**FlauBERT software**](https://github.com/getalp/Flaubert) using the same parameters as the [flaubert-base-uncased](https://huggingface.co/flaubert/flaubert_base_uncased) model (12 layers, 12 attention heads, 768 dims, 137M parameters, uncased).
  
 ## Available FlauBERT-Oral models
 
-`flaubert-oral-asr` : trained from scratch on ASR data, keeping the BPE tokenizer and vocabulary of flaubert-base-uncased
-`flaubert-oral-asr_nb` : trained from scratch on ASR data, BPE tokenizer is also trained on the same corpus
-`flaubert-oral-mixed` : trained from scratch on a mixed corpus of ASR and text data, BPE tokenizer is also trained on the same corpus
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+- `flaubert-oral-asr` : trained from scratch on ASR data, keeping the BPE tokenizer and vocabulary of flaubert-base-uncased
+- `flaubert-oral-asr_nb` : trained from scratch on ASR data, BPE tokenizer is also trained on the same corpus
+- `flaubert-oral-mixed` : trained from scratch on a mixed corpus of ASR and text data, BPE tokenizer is also trained on the same corpus