Update README.md

README.md

@@ -1,32 +1,20 @@
 ---
 license: apache-2.0
+datasets:
+- projecte-aina/CA-DE_Parallel_Corpus
+language:
+- ca
+- de
+metrics:
+- bleu
+library_name: fairseq
 ---
 ## Projecte Aina’s Catalan-German 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 Catalan-German datasets, which after filtering and cleaning comprised 6.258.272 sentence pairs. The model was evaluated on the Flores and NTREX evaluation datasets. 
+This model was trained from scratch using the [Fairseq toolkit](https://fairseq.readthedocs.io/en/latest/) on a combination of Catalan-German datasets, 
+which after filtering and cleaning comprised 6.258.272 sentence pairs. The model was evaluated on the Flores 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-ca-de", revision="main")
+model_dir = snapshot_download(repo_id="projecte-aina/aina-translator-ca-de", revision="main")
 
 tokenizer=pyonmttok.Tokenizer(mode="none", sp_model_path = model_dir + "/spm.model")
 tokenized=tokenizer.tokenize("Benvingut al projecte Aina!")
@@ -56,6 +44,10 @@ 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
@@ -78,19 +70,24 @@ The model was trained on a combination of the following datasets:
 | Tilde | 3.434.091 | 3.434.091 |
 | **Total**     	| **7.427.843** | **6.258.272** |
 
-All corpora except Europarl and Tilde were collected from [Opus](https://opus.nlpl.eu/). The Europarl and Tilde corpora are synthetic parallel corpora created from the original Spanish-Catalan corpora by [SoftCatalà](https://github.com/Softcatala).
+All corpora except Europarl and Tilde were collected from [Opus](https://opus.nlpl.eu/). 
+The Europarl and Tilde corpora are synthetic parallel corpora created from the original Spanish-Catalan corpora by [SoftCatalà](https://github.com/Softcatala).
 
 
 ### 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 6.159.631 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 6.159.631 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
 
@@ -124,13 +121,14 @@ The model was trained for a total of 22.000 updates. Weights were saved every 10
 
 ### Variable and metrics
 
-We use the BLEU score for evaluation on the [Flores-101](https://github.com/facebookresearch/flores) and [NTREX](https://github.com/MicrosoftTranslator/NTREX) test sets
+We use the BLEU score for evaluation on the [Flores-101](https://github.com/facebookresearch/flores) and [NTREX](https://github.com/MicrosoftTranslator/NTREX) test sets.
 
 ### Evaluation results
 
-Below are the evaluation results on the machine translation from Catalan to German compared to [Softcatalà](https://www.softcatala.org/) and [Google Translate](https://translate.google.es/?hl=es):
+Below are the evaluation results on the machine translation from Catalan to German compared to [Softcatalà](https://www.softcatala.org/) 
+and [Google Translate](https://translate.google.es/?hl=es):
 
-| Test set         	| SoftCatalà | Google Translate |mt-aina-ca-de|
+| Test set         	| SoftCatalà | Google Translate | aina-translator-ca-de |
 |----------------------|------------|------------------|---------------|
 | Flores 101 dev   	| 26,2     	| **34,8**     	| 27,5     	|
 | Flores 101 devtest   |26,3   	| **34,0**     	| 26,9     	|
@@ -142,20 +140,17 @@ Below are the evaluation results on the machine translation from Catalan to Germ
 ### Author
 Language Technologies Unit (LangTech) at the Barcelona Supercomputing Center
 
-### Contact information
+### Contact
 For further information, please send an email to langtech@bsc.es.
 
 ### Copyright
 Copyright Language Technologies Unit at Barcelona Supercomputing Center (2023)
 
-### Licensing information
+### License
 This work is licensed under a [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)
 
 ### Funding
-This work has been promoted and financed by the Generalitat de Catalunya through the [Aina project] (https://projecteaina.cat/).
-
-## 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