Update README.md

README.md

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 The available datasets are the CallHome (Japanese, Chinese, German, Spanish, English), AMI Corpus (English), Vox-Converse (English) and Simsamu (French). We aim to add more datasets in the future to better support speaker diarising on the Hub.
 
-- A collection of multilingual [fine-tuned segmentation model](https://huggingface.co/collections/diarizers-community/models-66261d0f9277b825c807ff2a) baselines compatible with pyannote. 
+- A collection of multilingual [fine-tuned segmentation model](https://huggingface.co/collections/diarizers-community/models-66261d0f9277b825c807ff2a) baselines compatible with pyannote.
 
-Each model has been fine-tuned on a specific Callhome language subset. They achieve better performances on multilingual data compared to pyannote's pre-trained [segmentation-3.0](https://huggingface.co/pyannote/segmentation-3.0) model: 
+Each model has been fine-tuned on a specific Callhome language subset. They achieve better performances on multilingual data compared to pyannote's pre-trained [segmentation-3.0](https://huggingface.co/pyannote/segmentation-3.0) model (see benchmark for more details on model performance). 
+
+Together with diarizers-community, we release: 
+
+- [diarizers](https://github.com/kamilakesbi/diarizers/tree/main), a library for fine-tuning pyannote speaker diarization models using the Hugging Face ecosystem.
+
+- A google colab [notebook](https://colab.research.google.com/github/kamilakesbi/notebooks/blob/main/fine_tune_pyannote.ipynb), with a step-by-step guide on how to use diarizers. 
 
 
+** Benchamrk: ** 
+
 | [Callhome](https://huggingface.co/datasets/diarizers-community/callhome) test dataset subset| Model    |     DER       |  False alarm  | Missed detection|   Confusion   | 
 | ------------------------|                                                                ------------- | ------------- | ------------- | --------------- | ------------- |
 |              Japanese   | [Pretrained](https://huggingface.co/pyannote/segmentation-3.0)               |     25.44     |      **2.30** |      17.45      |     5.69      |
@@ -33,11 +41,6 @@ Each model has been fine-tuned on a specific Callhome language subset. They achi
 
 Results are in %. They have been obtained using the [test script](https://github.com/kamilakesbi/diarizers/blob/main/test_segmentation.py) from diarizers. 
 
-Together with diarizers-community, we release: 
-
-- [diarizers](https://github.com/kamilakesbi/diarizers/tree/main), a library for fine-tuning pyannote speaker diarization models using the Hugging Face ecosystem.
-
-- A google colab [notebook](https://colab.research.google.com/github/kamilakesbi/notebooks/blob/main/fine_tune_pyannote.ipynb), with a step-by-step guide on how to use diarizers. 
 
 
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