readme update

README.md

@@ -610,11 +610,11 @@ The distilled model performs to within 1% WER of Whisper large-v3 on out-of-dist
 
 ### Evaluation methodology
 
-The model has been tested for both in-distribution (Common Voice 17 and Multilingual Librispeech) and out-of-distribution (Fleurs, Voxpopuli, custom [long-form test set](https://huggingface.co/datasets/speech-recognition-community-v2/dev_data)) short-form and long-form transcription performances.
+The model has been tested for both in-distribution (Common Voice 17 and Multilingual Librispeech) and out-of-distribution (Fleurs, Voxpopuli, custom [long-form test set](https://huggingface.co/datasets/speech-recognition-community-v2/dev_data)) short-form and long-form transcription performances. Models have been evaluated with SDPA, float32 and batch size 32. 
 
 **Short-form evaluations** are conducted on the four given datasets by first applying a filter to exclude samples longer than 30 seconds.
 
-**Long-form evaluation** is conducted on a custom out-of-distribution [long-form test set](https://huggingface.co/datasets/eustlb/french-long-form-test).
+**Long-form evaluation** is conducted on a custom out-of-distribution [long-form test set](https://huggingface.co/datasets/eustlb/french-long-form-test) using OpenAI's sequential long-form transcription algorithm (see [Sequential Long-Form](#sequential-long-form) section) with long form generation parameters that can be found [here](https://github.com/huggingface/distil-whisper/blob/a5ed489ba6edb405ecef334ba0feec1bdca7a948/training/run_eval.py#L670C5-L676C6).  
 
 ### Short-Form