language:
- en
- es
- ca
licence:
- apache-2.0
tags:
- aguila
- falcon
- spanish
- catalan
metrics:
- ppl
model-index:
- name: aguila_7b
results:
- task:
name: Causal Language Modeling
type: text-generation
metrics:
- name: Perplexity
type: ppl
value: 8.59
pipeline_tag: text-generation
widget:
- text: |-
Respòn a la pregunta següent.
Pregunta: "Quina és la capital de Suècia?"
Resposta: "La capital de Suècia és Estocolm."
----
Respòn a la pregunta següent.
Pregunta: "Quina beguda es consumeix als matins per despertar-se?"
Resposta: "La majoria de gent consumeix cafè per despertar-se."
----
Respòn a la pregunta següent.
Pregunta: "Explica com funciona un motor de combustió"
Resposta:
example_title: Pregunta-Resposta
- text: >-
Extrae las entidades nombradas del siguiente texto:
Texto: "Me llamo Wolfgang y vivo en Berlin"
Entidades: Wolfgang:PER, Berlin:LOC
----
Extrae las entidades nombradas del siguiente texto:
Texto: "Hoy voy a visitar el parc güell tras salir del barcelona
supercomputing center"
Entidades: parc güell:LOC, barcelona supercomputing center:LOC
----
Extrae las entidades nombradas del siguiente texto:
Texto: "Maria y Miguel no tienen ningún problema contigo"
Entidades: Maria:PER, Miguel:PER
----
Extrae las entidades nombradas del siguiente texto:
Texto: "Damián se cortó el pelo"
Entidades: Damián:PER
----
Extrae las entidades nombradas del siguiente texto:
Texto: "Lo mejor de Barcelona és el bar de mi amigo Pablo"
Entidades: Pablo:PER, Barcelona:LOC
----
Extrae las entidades nombradas del siguiente texto:
Texto: "Carlos comparte piso con Marc"
Entidades:
example_title: Entidades-Nombradas
Ǎguila-7B
Table of Contents
Click to expand
Model description
Ǎguila-7B is a transformer-based causal language model for Catalan, Spanish, and English. It is based on the 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 for downstream tasks.
How to use
Here is how to use this model:
import torch
import transformers
from transformers import AutoTokenizer, AutoModelForCausalLM
input_text = "Maria y Miguel no tienen ningún "
model = "projecte-aina/aguila-7b"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
torch_dtype=torch.bfloat16,
trust_remote_code=True,
device_map="auto",
)
generation = pipeline(
input_text,
max_length=200,
do_sample=True,
top_k=10,
eos_token_id=tokenizer.eos_token_id,
)
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.
Language adaptation
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 post.
Training
Training data
The training corpus consists of 26B tokens of several corpora gathered from web crawlings and public domain data.
| Dataset | Language | Tokens (per-epoch) | Epochs |
|---|---|---|---|
| Wikipedia | en | 2169.97M | 1.428144485 |
| C4_es | es | 53709.80M | 0.1049686196 |
| Biomedical | es | 455.03M | 0.7140722425 |
| Legal | es | 995.70M | 0.7140722425 |
| Wikipedia | es | 693.60M | 1.428144485 |
| Gutenberg | es | 53.18M | 0.7140722425 |
| C4_ca | ca | 2826.00M | 2.142216727 |
| Biomedical | ca | 11.80M | 1.428144485 |
| RacoCatalá Noticias | ca | 17.16M | 2.142216727 |
| RacoCatalá Forums | ca | 333.73M | 2.142216727 |
| CaWaC | ca | 57.79M | 2.142216727 |
| Wikipedia | ca | 228.01M | 3.570361212 |
| Vilaweb | ca | 50.34M | 2.142216727 |
The dataset has the following language distribution:
| Language | Percentage |
|---|---|
| En | 16.84% |
| Es | 41.38% |
| Ca | 41.79% |
Note: A small amount of English data was kept to avoid catastrophic forgetting.
Training procedure
The training corpus has been tokenized using a byte version of Byte-Pair Encoding (BPE) with a vocabulary size of 50,257 tokens. After training a new tokenizer and adapting falcon-7b's embedding layer, the model was further pre-trained in three target languages: Catalan, Spanish and English.
The training lasted a total of 320 hours on 8 NVIDIA H100 GPUs with 80GB RAM.
Training hyperparameters
- 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
- betas: (0.9,0.999)
- epsilon: 1e-08
- learning_rate: 5e-05
- lr_scheduler_type: linear
- num_epochs: 1.0
Framework versions
- Pytorch 2.0.0
- Transformers 4.30.2
- Datasets 2.13.1
- Tokenizers 0.13.3
Additional information
Author
The Language Technologies Unit from Barcelona Supercomputing Center.
Contact
For further information, please send an email to [email protected].
Copyright
Copyright(c) 2023 by Language Technologies Unit, Barcelona Supercomputing Center.
License
Funding
This work was partially funded by:
- The Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya within the framework of Projecte AINA.
- The Spanish State Secretariat for Digitalization and Artificial Intelligence within the framework of the Plan de Impulso de las Tecnologías del Lenguaje.
Disclaimer
Click to expand
The model published in this repository is intended for a generalist purpose and is available to third parties under a permissive Apache License, Version 2.0.
Be aware that the model may have biases and/or any other undesirable distortions.
When third parties deploy or provide systems and/or services to other parties using this model (or any system 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 model (Barcelona Supercomputing Center) be liable for any results arising from the use made by third parties.