Carballo-bloom-1.3B/README.md

language, tags, license, inference, widget

language	tags	license	inference	widget
gl	galician FLOR bloom	apache-2.0	parameters top_k do_sample temperature 10 true 0.4	text Traduce ao galego esta frase en inglés: Inglés: "my sister is studying Biology at the university." Galego: "a miña irmá está a estudar bioloxía na universidade."

Traduce ao galego esta frase en inglés:
Inglés: "You are working with my mother on a very interesting project."
Galego: "Estás a traballar coa miña nai nun proxecto moi interesante"
----
Traduce ao galego esta frase en inglés:
Inglés: "You have to fix the computer now"
Galego:

example_title: Translation

• text: |- Responde á seguinte pregunta. Pregunta: "Cal é a capital de Noruega?" Resposta: "A capital de Noruega é Oslo."

  Responde á seguinte pregunta. Pregunta: "Cal é a moeda de Portugal" Resposta: "A moeda de Portugal é o euro."

  Responde á seguinte pregunta. Pregunta: "Cal é a capital de Suecia?" Resposta: example_title: Question&Answering

• text: |- Cualifica como Positivo ou Negativo o sentimento da seguinte frase: Texto: "Estou moi feliz" Polaridade: Positivo

  Cualifica como Positivo ou Negativo o sentimento da seguinte frase: Texto: "Non me gusta beber cervexa" Polaridade: Negativo

  Cualifica como Positivo ou Negativo o sentimento da seguinte frase: Texto: "O meu pai detesta o seu traballo" Polaridade: Negativo

  Cualifica como Positivo ou Negativo o sentimento da seguinte frase: Texto: "Uxía desfruta xogando ao fútbol" Polaridade: Positivo

  Cualifica como Positivo ou Negativo o sentimento da seguinte frase: Texto: "O neno non está contento coas notas" Polaridade: example_title: Sentiment Analysis

• text: |- Extrae as entidades nomeadas do seguinte texto: Texto: "Chámome Wolfgang e vivo en Berlin" Entidades: Wolfgang:PER, Berlin:LOC

  Extrae as entidades nomeadas do seguinte texto: Texto: "María e Miguel non teñen ningún problema" Entidades: María:PER, Miguel:PER

  Extrae as entidades nomeadas do seguinte texto: Texto: "O mellor de Barcelona é o bar do meu amigo Pablo" Entidades: Pablo:PER, Barcelona:LOC

  Extrae as entidades nomeadas do seguinte texto: Texto: "Carlos comparte cuarto con Marc" Entidades: example_title: Name Entity Recognition (NER)
• text: A receita tradicional das filloas é example_title: Filloas
• text: O neno vivía preto de example_title: O neno datasets:
• proxectonos/corpusnos

---

Carballo-bloom-1.3B

Table of Contents

Click to expand

• Carballo-bloom-1.3B
  • Table of Contents
  • Model description
  • Intended uses and limitations
  • How to use
  • Training
    • Tools
    • Language adaptation and training
    • Training data
    • Training hyperparameters
    • Framework
  • Evaluation
  • Additional information
    • Contact
    • Copyright
    • License
    • Funding
  • Citation information

Model description

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 (developed by AINA Project and based in BLOOM-1.7B) with the galician corpus CorpusNos.

Intended uses and limitations

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

import torch
from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM

input_text = "Hoxe fai un bo día. O sol  "

model_id  = "proxectonos/Carballo-bloom-1.3B"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto",
)
generation = generator(
    input_text,
    do_sample=True,
    top_k=10,
    eos_token_id=tokenizer.eos_token_id
)

print(f"Result: {generation[0]['generated_text']}")

Training

Tools

It was trained using HuggingFace Transformers and Pytorch, using the Causal Modeling Language script

Language adaptation and training

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. 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 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.

Training data

CorpusNÓS is a massive Galician corpus made up of 2.1B words primarily devised for training large language models. The corpus sources are varied and represent a relatively wide range of genres.

The corpus is structured as follows:

Subcorpus	Genre	Nº tokens	Nº documents
Data obtained via transfer agreement	Books	7,255,784	104
	Research articles	2,665,351	664
	Press	124,253,084	224,419
	Governmental	245,897,880	654,505
	Web contents	15,946,686	44,165
	Encyclopedic	4,799,214	47,396
	Subtotal	400,817,999	971,253

Subcorpus	Genre	Nº tokens	Nº documents
Public data	Press and blogs	153,497,883	665,265
	Encyclopedic	57,164,848	184,628
	Web crawls	1,384,015,664	3,366,449
	Translation corpora	133,726,004	4,745,799
	Subtotal	1,728,404,399	8,777,514
	Total	2,129,222,398	9,748,767
Download (Zenodo)	https://zenodo.org/records/10687642

Training hyperparameters

• seed: 42
• num_devices: 1
• train_batch_size: 2
• eval_batch_size: 2
• gradient_acummulation: 4
• optimizer: AdamW
• betas: (0.9,0.999)
• epsilon: 1e-08
• weight_decay_rate: 0.1
• scheduler: "Linear"
• learning_rate: 5e-05
• num_epochs: 1.2

Framework

The traininf was conducted in the Galicia Supercomputing Center (CESGA), using 1 node with 5 GPUs NVIDIA A100.

Evaluation

Model	Belebele	CoLA	OpenBookQA	Parafrases-gl	PAWS-X
Carballo-Bloom	0.231±0.014	0.499±0.012	0.364±0.022	0.523±0.031	0.541±0.011
Carballo-Cerebras	0.271±0.015	0.502±0.012	0.368±0.022	0.496±0.031	0.531±0.011
Bloom-1b1	0.234±0.014	0.507±0.012	0.338±0.021	0.485±0.031	0.508±0.011
Bloom-1b7	0.218±0.014	0.500±0.012	0.338±0.021	0.539±0.031	0.539±0.011
mGPT	0.229±0.014	0.494±0.012	0.332±0.021	0.423±0.031	0.517±0.011
Flor-1.3B	0.220±0.014	0.504±0.012	0.342±0.021	0.516±0.031	0.536±0.011
Cerebras-1.3B	0.221±0.014	0.497±0.012	0.300±0.021	0.492±0.031	0.531±0.011

Additional information

Contact

For further information, please send an email to proxecto.nos@usc.gal

Funding

This model was development within the Nós Project, funded by the Ministerio para la Transformación Digital y de la Función Pública - Funded by EU – NextGenerationEU within the framework of the project ILENIA with reference 2022/TL22/00215336.

How to cite this work

if you use this model, please cite this article:

Gamallo, Pablo, Pablo Rodríguez Fernández, Iria de Dios Flores, Susana Sotelo, Silvia Paniagua, José Ramom Pichel, Daniel Bardanca, Marcos Garcia (2024) "Open Generative Large Language Models for Galician", Procesamiento del Lenguaje Natural, 73, pp. 259-270. ISSN: 1135-5948.