Update README.md

README.md

@@ -81,9 +81,9 @@ pipeline_tag: text-generation
   - [Training procedure](#training-procedure)
 - [Additional information](#additional-information)
   - [Author](#author)
-  - [Contact information](#contact-information)
+  - [Contact](#contact)
   - [Copyright](#copyright)
-  - [Licensing information](#licensing-information)
+  - [License](#license)
   - [Funding](#funding)
   - [Disclaimer](#disclaimer)
 
@@ -91,12 +91,15 @@ pipeline_tag: text-generation
 
 ## Model description
 
-The **Ǎguila-7B** is a transformer-based causal language model for Catalan, Spanish, and English. It is based on the [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b) model and has been trained on a 26B token trilingual corpus collected from publicly available corpora and crawlers.
+The **Ǎguila-7B** is a transformer-based causal language model for Catalan, Spanish, and English. 
+It is based on the [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b) model and has been trained on a 26B token 
+trilingual corpus collected from publicly available corpora and crawlers.
 
 
 ## Intended uses and limitations
 
-The **Ǎguila-7B** model is ready-to-use only for causal language modeling to perform text-generation tasks. However, it is intended to be fine-tuned on a generative downstream task.
+The **Ǎguila-7B** model is ready-to-use only for causal language modeling to perform text-generation tasks. 
+However, it is intended to be fine-tuned on a generative downstream task.
 
 ## How to use
 
@@ -131,7 +134,9 @@ print(f"Result: {generation['generated_text']}")
 ```
 
 ## 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 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. 
+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 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. 
 
 
 ## Language adaptation
@@ -142,7 +147,7 @@ We adapted the original Falcon-7B model to Spanish and Catalan by swapping the t
 
 ### Training data
 
-The training corpus consists 26B tokens of several corpora gathered from web crawlings and public corpora.
+The training corpus consists 26B tokens of several corpora gathered from web crawlings and public domain data.
 
 | Dataset             | Language | Tokens (per-epoch) | Epochs       |
 |---------------------|----------|--------------------|--------------|
@@ -170,21 +175,26 @@ The dataset has the following language distribution:
 
 ## Training procedure
 
-The training corpus has been tokenized using a byte version of [Byte-Pair Encoding (BPE)](https://github.com/openai/gpt-2) used in the original [RoBERTA](https://github.com/pytorch/fairseq/tree/master/examples/roberta) model with a vocabulary size of 50,257 tokens. Once the model has been successfully initialized, we continued its pre-training in the three target languages: Catalan, Spanish, and English. We kept a small amount of English in order to avoid catastrophic forgetting. The training lasted a total of 320 hours with 8 NVIDIA H100 GPUs of 80GB of RAM. 
+The training corpus has been tokenized using a byte version of [Byte-Pair Encoding (BPE)](https://github.com/openai/gpt-2) used 
+in the original [RoBERTA](https://github.com/pytorch/fairseq/tree/master/examples/roberta) model with a vocabulary size of 50,257 tokens. 
+Once the model has been successfully initialized, we continued its pre-training in the three target languages: Catalan, Spanish, and English. 
+We kept a small amount of English data in order to avoid catastrophic forgetting. 
+The training lasted a total of 320 hours on 8 NVIDIA H100 GPUs with 80GB RAM.
 
 
 ### Training hyperparameters
 
-The following hyperparameters were used during training:
-- learning_rate: 5e-05
-- train_batch_size: 1
-- eval_batch_size: 1
 - seed: 42
 - distributed_type: multi-GPU
 - num_devices: 8
+- train_batch_size: 1
+- eval_batch_size: 1
 - total_train_batch_size: 8
 - total_eval_batch_size: 8
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- optimizer: Adam
+- betas=(0.9,0.999)
+- epsilon=1e-08
+- learning_rate: 5e-05
 - lr_scheduler_type: linear
 - num_epochs: 1.0
 
@@ -199,29 +209,31 @@ The following hyperparameters were used during training:
 ## Additional information
 
 ### Author
-Language Technologies Unir at the Barcelona Supercomputing Center (langtech@bsc.es).
+The Language Technologies Unit from Barcelona Supercomputing Center.
 
-### Contact information
-For further information, send an email to aina@bsc.es.
+### Contact
+For further information, please send an email to <langtech@bsc.es>.
 
 ### Copyright
 Copyright (c) 2023 Langtech Unit at Barcelona Supercomputing Center.
 
-### Licensing information
+### License
 [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)
 
 ### Funding
-This work was funded by the [Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya](https://politiquesdigitals.gencat.cat/ca/inici/index.html#googtrans(ca|en) within the framework of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina). This work was also partially funded by the [Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA)](https://portal.mineco.gob.es/en-us/digitalizacionIA/Pages/sedia.aspx) within the framework of the Plan-TL.
+This work was partially funded by:
+  - The [Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya](https://politiquesdigitals.gencat.cat/ca/inici/index.html#googtrans(ca|en) within the framework of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina).
+  - The [Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA)](https://portal.mineco.gob.es/en-us/digitalizacionIA/Pages/sedia.aspx) within the framework of the [Plan-TL](https://plantl.mineco.gob.es/Paginas/index.aspx).
 
 ### 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.
+The model published in this repository is intended for a generalist purpose and is available to third parties. This 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.
+When third parties deploy or provide systems and/or services to other parties using this model (or using systems 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 models (BSC – Barcelona Supercomputing Center) be liable for any results arising from the use made by third parties of these models.
+In no event shall the owner and creator of the model (BSC – Barcelona Supercomputing Center) be liable for any results arising from the use made by third parties.
 
 </details>