Update README.md

README.md

@@ -84,13 +84,13 @@ widget:
   example_title: O neno
 ---
 
-# FLOR-1.3B-GL
+# Carballo-bloom-1.3B
 
 ## Table of Contents
 <details>
 <summary>Click to expand</summary>
 
-- [FLOR-1.3B-GL](#flor-13b-gl)
+- [Carballo-bloom-1.3B](#carballo-bloom-13)
   - [Table of Contents](#table-of-contents)
   - [Model description](#model-description)
   - [Intended uses and limitations](#intended-uses-and-limitations)
@@ -113,12 +113,12 @@ widget:
 
 ## Model description
 
-**FLOR-1.3B-GL** is a 1.3B-parameter transformer-based causal language model for Galician. 
+**Carballo-bloom-1.3B** is a 1.3B-parameter transformer-based causal language model for Galician. 
 It is the result of a continual pretraining of [FLOR-1.3B](https://huggingface.co/projecte-aina/FLOR-1.3B) (developed by [AINA Project](https://projecteaina.cat/) and based in [BLOOM-1.7B](https://huggingface.co/bigscience/bloom-1b7)) with the galician corpus [CorpusNos](https://zenodo.org/records/10687642).
 
 ## Intended uses and limitations
 
-The **FLOR-1.3B-GL** model is ready-to-use only for causal language modeling. 
+The **Carballo-bloom-1.3B** model is ready-to-use only for causal language modeling. 
 It can perform text-generation tasks and be fine-tuned for specific scenarios.
 
 ## How to use
@@ -128,7 +128,7 @@ from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
 
 input_text = "Hoxe fai un bo día. O sol  "
 
-model_id  = "proxectonos/FLOR-1.3B-GL"
+model_id  = "proxectonos/Carballo-bloom-1.3B"
 tokenizer = AutoTokenizer.from_pretrained(model_id)
 model = AutoModelForCausalLM.from_pretrained(model_id)
 generator = pipeline(
@@ -157,10 +157,10 @@ It was trained using HuggingFace Transformers and Pytorch, using the [Causal Mod
 
 ### Language adaptation and training
 
-The language adaptation technique used to train FLOR-1.3B-GL is based in the used to train FLOR-1.3B, which is explained by their authors in this [Medium Post](https://medium.com/@mpamies247/flor-6-3b-a-chinchilla-compliant-model-for-catalan-spanish-and-english-7cdb389a9aac). In summary, we proceeded as follows:
+The language adaptation technique used to train Carballo-bloom-1.3B is based in the used to train FLOR-1.3B, which is explained by their authors in this [Medium Post](https://medium.com/@mpamies247/flor-6-3b-a-chinchilla-compliant-model-for-catalan-spanish-and-english-7cdb389a9aac). In summary, we proceeded as follows:
 1) We trained our own BPE tokenizer for galician and replaced the original FLOR-1.3B tokenizer and vocabulary with it. 
 2) The embeddings corresponding to tokens that are present in both the original and the target vocabulary (matching tokens) were used for initialization.
-3) The embeddings from tokens not present in FLOR-1.3-GL's original vocabulary were initialized as the average of all embeddings.
+3) The embeddings from tokens not present in Carballo-bloom-1.3B's original vocabulary were initialized as the average of all embeddings.
 4) The model was initialized with the weights from FLOR-1.3B and with our adapted tokenizer (step 1) and embeddings (steps 2-3).
 5) The model was then trained on a galician corpus.