Update README.md

README.md

@@ -13,21 +13,31 @@ datasets:
 - juanjucm/FLEURS-SpeechT-GL-EN
 language:
 - gl
+- en
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
 
 # nllb-200-distilled-600M-FLEURS-GL-EN
 
-This model is a fine-tuned version of [facebook/nllb-200-distilled-600M](https://huggingface.co/facebook/nllb-200-distilled-600M) on [juanjucm/FLEURS-SpeechT-GL-EN](https://huggingface.co/datasets/juanjucm/FLEURS-SpeechT-GL-EN).
+This model is a fine-tuned version of [facebook/nllb-200-distilled-600M](https://huggingface.co/facebook/nllb-200-distilled-600M) trained on [juanjucm/FLEURS-SpeechT-GL-EN](https://huggingface.co/datasets/juanjucm/FLEURS-SpeechT-GL-EN) for **Galician-to-Englis Machine Translation** task. It takes Galician texts as input and generates the correspondant English translation.
+
+This Machine Translation model, was developed to be the second stage of a Speech Translation cascade system for transcribing and translating Galician audios into English texts. [This STT model](https://huggingface.co/juanjucm/whisper-large-v3-turbo-FLEURS-GL) can be used as a first step to transcribe Galician audio into text. After that, this MT model can be applied over the generated Galician transcriptions to get English text translations.
+The motivation behind this work is to increase the visibility of the Galician language, making it more accessible for non-Galician speakers to understand and engage with Galician audio content.
+
+This model was developed during a 3-week Speech Translation workshop organised by [Yasmin Moslem](https://huggingface.co/ymoslem).
+
+
+
+### Performance and training details
+
+Baseline model achieved a BLEY score of **39.98** on the evaluation dataset.
+
 It achieves the following results on the evaluation set:
 - Loss: 0.0877
-- Bleu: 43.7516
-
-### Training hyperparameters
+- **BLEU: 43.7516**
 
 The following hyperparameters were used during training:
+
 - learning_rate: 1e-05
 - train_batch_size: 4
 - eval_batch_size: 4
@@ -40,6 +50,8 @@ The following hyperparameters were used during training:
 
 ### Training results
 
+We used [BLEU Score](https://en.wikipedia.org/wiki/BLEU) as our reference translation metric for selecting the best checkpoint after training.
+
 | Training Loss | Epoch | Step | Validation Loss | Bleu    |
 |:-------------:|:-----:|:----:|:---------------:|:-------:|
 | No log        | 1.0   | 343  | 5.3970          | 42.8151 |