Update README.md

README.md

@@ -49,7 +49,7 @@ The following people contributed to building this model: Rolv-Arild Braaten, Per
 ## Training procedure
 To reproduce these results, we strongly recommend that you follow the [instructions from HuggingFace](https://github.com/huggingface/transformers/tree/master/examples/research_projects/robust-speech-event#talks) to train a simple Swedish model.
 
-When you have verified that you are able to do this, create a new repo. You can then start by copying the files **run.sh** and **run_speech_recognition_ctc.py** from our repo. You should be able to reproduce our results by just running this script. With some tweaking, you will most likely be able to build an even better ASR.
+When you have verified that you are able to do this, create a fresh new repo. You can then start by copying the files **run.sh** and **run_speech_recognition_ctc.py** from our repo. You  Rhould be able to reproduce our results by just running this script. With some tweaking, you will most likely be able to build an even better ASR.
 
 ### 5-gram Language Model
 Adding a language model will improve the results of the model. 🤗 has provided another [very nice blog](https://huggingface.co/blog/wav2vec2-with-ngram) about how to add a 5-gram language model to improve the ASR model. You can build this from your own corpus, for instance by extracting some suitable text from the [Norwegian Colossal Corpus](https://huggingface.co/datasets/NbAiLab/NCC). You can also skip some of the steps in the guide, and copy the [5-gram model from this repo](https://huggingface.co/NbAiLab/XLSR-300M-bokmaal/tree/main/language_model).
@@ -100,10 +100,13 @@ The following parameters were used during training:
 --preprocessing_num_workers="16"
 ```
 
-This training will take 3-4 days on an average GPU. You might get a decent model and faster results by changing these parameters:
-```
---per_device_train_batch_size - Adjust this to the maximum of available memory. 16 or 24 might be good settings depending on your system
---gradient_accumulation_steps - Can be adjusted even further up to increase batch size and speed up training without running into memory issues
---learning_rate - Can be increased, maybe as high as 1e-4. Speeds up training but might add instability
---epochs - Can be decreased significantly. This is a huge dataset and you might get a decent result already after a couple of epochs
-```
+Following this settings, the training might take 3-4 days on an average GPU. You should however get a decent model and faster results by changing these parameters
+
+| Parameter| Comment | 
+|:-------------|:-----|
+| per_device_train_batch_size       | Adjust this to the maximum of available memory. 16 or 24 might be good settings depending on your system  |
+|gradient_accumulation_steps |Can be adjusted even further up to increase batch size and speed up training without running into memory issues |
+| learning_rate|Can be increased, maybe as high as 1e-4. Speeds up training but might add instability |
+| epochs| Can be decreased significantly. This is a huge dataset and you might get a decent result already after a couple of epochs|
+
+