readme

README.md

@@ -11,6 +11,8 @@ pipeline_tag: fill-mask
 **This model relies on a custom modeling file, you need to add trust_remote_code=True**\
 **See [\#13467](https://github.com/huggingface/transformers/pull/13467)**
 
+Conversion script is available at this [link](https://github.com/ccdv-ai/convert_checkpoint_to_lsg).
+
 * [Usage](#usage)
 * [Parameters](#parameters)
 * [Sparse selection type](#sparse-selection-type)
@@ -19,12 +21,9 @@ pipeline_tag: fill-mask
 
 This model is adapted from [XLM-RoBERTa-base](https://huggingface.co/xlm-roberta-base) model without additional pretraining yet. It uses the same number of parameters/layers and the same tokenizer. 
 
-
 This model can handle long sequences but faster and more efficiently than Longformer or BigBird (from Transformers) and relies on Local + Sparse + Global attention (LSG).
 
-
-The model requires sequences whose length is a multiple of the block size. The model is "adaptive" and automatically pads the sequences if needed (adaptive=True in config). It is however recommended, thanks to the tokenizer, to truncate the inputs (truncation=True) and optionally to pad with a multiple of the block size (pad_to_multiple_of=...). \
-
+The model requires sequences whose length is a multiple of the block size. The model is "adaptive" and automatically pads the sequences if needed (adaptive=True in config). It is however recommended, thanks to the tokenizer, to truncate the inputs (truncation=True) and optionally to pad with a multiple of the block size (pad_to_multiple_of=...). 
 
 Support encoder-decoder but I didnt test it extensively.\
 Implemented in PyTorch.