Update README.md

README.md

@@ -1,32 +1,20 @@
 ---
 license: apache-2.0
+language:
+- gl
+- ca
+metrics:
+- bleu
+library_name: fairseq
 ---
 ## Projecte Aina’s Galician-Catalan machine translation model
-
-## Table of Contents
-- [Model Description](#model-description)
-- [Intended Uses and Limitations](#intended-use)
-- [How to Use](#how-to-use)
-- [Training](#training)
-  - [Training data](#training-data)
-  - [Training procedure](#training-procedure)
-	- [Data Preparation](#data-preparation)
-	- [Tokenization](#tokenization)
-	- [Hyperparameters](#hyperparameters)
-- [Evaluation](#evaluation)
-   - [Variable and Metrics](#variable-and-metrics)
-   - [Evaluation Results](#evaluation-results)
-- [Additional Information](#additional-information)
-  - [Author](#author)
-  - [Contact Information](#contact-information)
-  - [Copyright](#copyright)
-  - [Licensing Information](#licensing-information)
-  - [Funding](#funding)
-  - [Disclaimer](#disclaimer)
  
 ## Model description
 
-This model was trained from scratch using the [Fairseq toolkit](https://fairseq.readthedocs.io/en/latest/) on a combination of Galician-Catalan datasets totalling 10.017.995 sentence pairs. 4.267.995 sentence pairs were parallel data collected from the web while the remaining 5.750.000 sentence pairs were parallel synthetic data created using the GL-ES translator of [Proxecto Nós](https://huggingface.co/proxectonos/Nos_MT-OpenNMT-es-gl). The model was evaluated on the Flores, TaCon and NTREX evaluation datasets. 
+This model was trained from scratch using the [Fairseq toolkit](https://fairseq.readthedocs.io/en/latest/) on a combination of Galician-Catalan datasets 
+totalling 10.017.995 sentence pairs. 4.267.995 sentence pairs were parallel data collected from the web while the remaining 5.750.000 sentence pairs 
+were parallel synthetic data created using the GL-ES translator of [Proxecto Nós](https://huggingface.co/proxectonos/Nos_MT-OpenNMT-es-gl). 
+The model was evaluated on the Flores, TaCon and NTREX evaluation datasets. 
 
 ## Intended uses and limitations
 
@@ -46,7 +34,7 @@ Translate a sentence using python
 import ctranslate2
 import pyonmttok
 from huggingface_hub import snapshot_download
-model_dir = snapshot_download(repo_id="projecte-aina/mt-aina-gl-ca", revision="main")
+model_dir = snapshot_download(repo_id="projecte-aina/aina-translator-gl-ca", revision="main")
 tokenizer=pyonmttok.Tokenizer(mode="none", sp_model_path = model_dir + "/spm.model")
 tokenized=tokenizer.tokenize("Benvido ao proxecto Ilenia.")
 translator = ctranslate2.Translator(model_dir)
@@ -54,6 +42,11 @@ translated = translator.translate_batch([tokenized[0]])
 print(tokenizer.detokenize(translated[0][0]['tokens']))
 ```
 
+
+## Limitations and bias
+At the time of submission, no measures have been taken to estimate the bias and toxicity embedded in the model. 
+However, we are well aware that our models may be biased. We intend to conduct research in these areas in the future, and if completed, this model card will be updated. 
+
 ## Training
 
 ### Training data
@@ -75,18 +68,23 @@ The Galician-Catalan data collected from the web was a combination of the follow
 | **Total**     	| **4.952.275** |
 
 The datasets were concatenated before filtering to avoid intra-dataset duplicates and the final size was 4.267.995.
-The 5.750.000 sentence pairs of synthetic parallel data were created from a random sampling of the [Projecte Aina ES-CA corpus](https://huggingface.co/projecte-aina/mt-aina-ca-es)
+The 5.750.000 sentence pairs of synthetic parallel data were created from a random sampling 
+of the [Projecte Aina ES-CA corpus](https://huggingface.co/projecte-aina/mt-aina-ca-es).
 
 ### Training procedure
 
 ### Data preparation
 
- All datasets are deduplicated and filtered to remove any sentence pairs with a cosine similarity of less than 0.75. This is done using sentence embeddings calculated using [LaBSE](https://huggingface.co/sentence-transformers/LaBSE). The filtered datasets are then concatenated to form a final corpus of 10.017.995 and before training the punctuation is normalized using a modified version of the join-single-file.py script from [SoftCatalà](https://github.com/Softcatala/nmt-models/blob/master/data-processing-tools/join-single-file.py)
+ All datasets are deduplicated and filtered to remove any sentence pairs with a cosine similarity of less than 0.75.
+ This is done using sentence embeddings calculated using [LaBSE](https://huggingface.co/sentence-transformers/LaBSE). 
+ The filtered datasets are then concatenated to form a final corpus of 10.017.995 and before training the punctuation is normalized using a 
+ modified version of the join-single-file.py script from [SoftCatalà](https://github.com/Softcatala/nmt-models/blob/master/data-processing-tools/join-single-file.py)
 
 
 #### Tokenization
 
- All data is tokenized using sentencepiece, with a 50 thousand token sentencepiece model  learned from the combination of all filtered training data. This model is included.  
+ All data is tokenized using sentencepiece, with a 50 thousand token sentencepiece model  learned from the combination of all filtered training data. 
+ This model is included.  
 
 #### Hyperparameters
 
@@ -119,33 +117,58 @@ This data was then concatenated with the synthetic parallel data and training co
 Weights were saved every 1000 updates and reported results are the average of the last 4 checkpoints.
 
 ## Evaluation
+
 ### Variable and metrics
-We use the BLEU score for evaluation on test sets: [Flores-200](https://github.com/facebookresearch/flores/tree/main/flores200), [TaCon](https://elrc-share.eu/repository/browse/tacon-spanish-constitution-mt-test-set/84a96138b98611ec9c1a00155d02670628f3e6857b0f422abd82abc3795ec8c2/) and [NTREX](https://github.com/MicrosoftTranslator/NTREX)
+
+We use the BLEU score for evaluation on test sets: [Flores-200](https://github.com/facebookresearch/flores/tree/main/flores200), 
+[TaCon](https://elrc-share.eu/repository/browse/tacon-spanish-constitution-mt-test-set/84a96138b98611ec9c1a00155d02670628f3e6857b0f422abd82abc3795ec8c2/) 
+and [NTREX](https://github.com/MicrosoftTranslator/NTREX).
+
 ### Evaluation results
-Below are the evaluation results on the machine translation from Galician to Catalan compared to [Google Translate](https://translate.google.com/), [M2M100 1.2B](https://huggingface.co/facebook/m2m100_1.2B), [NLLB 200 3.3B](https://huggingface.co/facebook/nllb-200-3.3B) and [ NLLB-200's distilled 1.3B variant](https://huggingface.co/facebook/nllb-200-distilled-1.3B):
-| Test set         	|Google Translate|M2M100 1.2B| NLLB 1.3B | NLLB 3.3 |mt-aina-gl-ca|
+
+Below are the evaluation results on the machine translation from Galician to Catalan compared to [Google Translate](https://translate.google.com/), 
+[M2M100 1.2B](https://huggingface.co/facebook/m2m100_1.2B), [NLLB 200 3.3B](https://huggingface.co/facebook/nllb-200-3.3B) and 
+[ NLLB-200's distilled 1.3B variant](https://huggingface.co/facebook/nllb-200-distilled-1.3B):
+
+| Test set         	|Google Translate|M2M100 1.2B| NLLB 1.3B | NLLB 3.3 | aina-translator-gl-ca |
 |----------------------|----|-------|-----------|------------------|---------------|
 |Flores 101 devtest   	|**36,4**|32,6| 22,3   	| 34,3   	| 32,4     	|
 | TaCON                 |48,4|56,5|32,2  	    | 54,1      	| **58,2**     	|
 | NTREX                 |**34,7**|34,0|20,4    	| 34,2     	| 33,7     	|
 | Average           	|39,0|41,0| 25,0 	| 40,9     	    | **41,4**      	|
+
+
 ## Additional information
+
 ### Author
-Language Technologies Unit (LangTech) at the Barcelona Supercomputing Center. 
-### Contact information
-For further information, send an email to <[email protected]>
+The Language Technologies Unit from Barcelona Supercomputing Center.
+
+### Contact
+For further information, please send an email to <[email protected]>.
+
 ### Copyright
-Copyright Language Technologies Unit at Barcelona Supercomputing Center (2023)
-### Licensing information
-This work is licensed under a [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)
+Copyright(c) 2023 by Language Technologies Unit, Barcelona Supercomputing Center.
+
+### License
+[Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)
+
 ### Funding
-This work is funded by the Ministerio para la Transformación Digital y de la Función Pública - Funded by EU – NextGenerationEU within the framework of the [project ILENIA](https://proyectoilenia.es/) with reference 2022/TL22/00215337, 2022/TL22/00215336, 2022/TL22/00215335 y 2022/TL22/00215334 
-## Limitations and Bias
-At the time of submission, no measures have been taken to estimate the bias and toxicity embedded in the model. However, we are aware that our models may be biased since the corpora have been collected using crawling techniques on multiple web sources. We intend to conduct research in these areas in the future, and if completed, this model card will be updated.
+This work has been promoted and financed by the Generalitat de Catalunya through the [Aina project](https://projecteaina.cat/).
+
 ### Disclaimer
+
 <details>
 <summary>Click to expand</summary>
-The models published in this repository are intended for a generalist purpose and are available to third parties. These models may have bias and/or any other undesirable distortions.
-When third parties, deploy or provide systems and/or services to other parties using any of these models (or using systems based on these models) or become users of the models, they should note that it is their responsibility to mitigate the risks arising from their use and, in any event, to comply with applicable regulations, including regulations regarding the use of Artificial Intelligence.
-In no event shall the owner and creator of the models (BSC – Barcelona Supercomputing Center) be liable for any results arising from the use made by third parties of these models.
-</details>
+
+The model published in this repository is intended for a generalist purpose and is available to third parties under a permissive Apache License, Version 2.0. 
+
+Be aware that the model may have biases and/or any other undesirable distortions.
+
+When third parties deploy or provide systems and/or services to other parties using this model (or any system based on it) 
+or become users of the model, they should note that it is their responsibility to mitigate the risks arising from its use and, 
+in any event, to comply with applicable regulations, including regulations regarding the use of Artificial Intelligence.
+
+In no event shall the owner and creator of the model (Barcelona Supercomputing Center) 
+be liable for any results arising from the use made by third parties.
+
+</details>