Update README.md

README.md

@@ -77,21 +77,16 @@ pipeline_tag: text-generation
 
 # falcon_7b_balanced_tokenizer_fp16_CPT_open_data_26B_tokens_balanced_es_ca
 
-## Overview
-
-This model is a new result towards the long-run problem of "What is the best strategy for training a model in my language (not English)?"
-
-This model adapts the [falcon-7b](https://huggingface.co/tiiuae/falcon-7b) to the new target languages Spanish and Catalan by swapping the tokenizer and adjusting the embedding layer before training with 26B tokens in the target languages.
+## Model description
 
-## Language Adaptation
+The **Cǒndor-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 trilugual corpus collected from publicly available corpora and crawlers.
 
-When adapting a model from English to other languages the tokenizer plays a crucial role. 
+## Language adaptation
 
-If the tokenizer does not include the target language in its training data, the resulting model will need many more tokens to perform the same task.
-We solve this problem by creating a new tokenizer in the target languages (Spanish and Catalan) and adapting the embedding layer to it.
+We adapted the original Falcon-7B model to Spanish and Catalan by swapping the tokenizer and adjusting the embedding layer. The adaptation procedure is explained in this [blog](https://medium.com/@mpamies247/ee1ebc70bc79).
 
-### New Tokenizer
-We trained a new BPE Tokenizer for the Catalan and Spanish languages (equal representation). We shuffle a small amount of English in the mixture (since English is in the model training data).
+### New vocabulary
+We trained a new BPE Tokenizer for the Catalan and Spanish languages (equal representation). We shuffled a small amount of English in the mixture (since English is in the model training data).
 The resulting data has the following language distribution:
 
 |Language|%|
@@ -100,15 +95,17 @@ The resulting data has the following language distribution:
 |Es|41.38%|
 |Ca|41.79%|
 
-*P.D: It was meant to be the same distribution as the model train data (presented in Continual Pre-Training section)*
-
-This reduces drastically the amount of tokens required to tokenize a text in the target languages (~70 %) while the English tokenization shows a small increase (~115 %).
+This reduced drastically the number of tokens required to tokenize a text in the target language while the English tokenization shows a small increase.
 
 ### Embedding Layer Initialization
 In order to fully take advantage of the English Pre-Training of the original Falcon model, we decided to re-use the embedding weights of the original model for those tokens shared between the two Tokenizers (the new and the old one). The rest of the embedding weights are initialized as the mean value of the weights of the original Tokenizer.
 
-### Continual Pre-Training
-Once the model has been successfully initialized, we continue its pre-training in the two target languages: Catalan and Spanish. We also shuffle a small amount of English in order to avoid catastrophic forgetting. The datasets used to train this model follow:
+
+## Training
+
+### Training data
+
+Once the model has been successfully initialized, we continue its pre-training in the two target languages: Catalan and Spanish. We also kept a small amount of English in order to avoid catastrophic forgetting. The composition of our 26B token dataset used to train this model is the following:
 
 | Dataset             | Language | Tokens (pre-epoch) | Epochs       |
 |---------------------|----------|--------------------|--------------|
@@ -134,20 +131,15 @@ The resulting dataset has the following language distribution:
 |Es|41.38%|
 |Ca|41.79%|
 
-## Model description
 
-More information needed
 
 ## Intended uses & limitations
 
-The 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 **Cǒndor-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.
 
 
 ## Limitations and biases
-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. 
 
 ## Training and evaluation data