Model Card for gemma-7b-it-legal-refugiados-es
Spain is the third country with the highest number of asylum applications, receiving each year approximately more than 100,000 applications, and the third with the lowest number of approvals within the EU.
The main objective of this project is to facilitate the tasks of NGOs in this field and other institutions and help them to obtain answers to questions (QA) related to refugee legislation in Spanish. With its refined understanding of the nuances and complexities of this legal field.
The objective of this model is to facilitate question answering (QA) tasks pertaining to Spanish refugee legislation. With its refined understanding of the nuances and intricacies of this legal domain
Model Details
Model Description
The objective of this model is to facilitate question answering (QA) tasks pertaining to Spanish refugee legislation. With its refined understanding of the nuances and intricacies of this legal domain.
This model is a fine-tuned version of google/gemma-7b on the dataset AsistenciaRefugiados.
This is the model card of a 🤗 transformers model that has been pushed on the Hub to allow public access.
Developed by: Alvaro Hidalgo Eduardo Muñoz Teresa Martin
Funded by: SomosNLP, HuggingFace
Model type: Language model, instruction tuned
Language(s): es-ES, es-MX, es-VE
License: apache-2.0
Fine-tuned from model: [google/gemma-7b](https://huggingface.co/google/
Dataset used: AsistenciaRefugiados
Model Sources
- Repository: Notebook in This repo
- Demo: Demo Space
- Video presentation: Youtube Video
Model Family
This model is a fine-tuned version of google/gemma-7b.
Uses
Direct Use
The primary objective of this model is to facilitate question answering (QA) tasks pertaining to Spanish refugee legislation. With its refined understanding of the nuances and intricacies of this legal domain.
Downstream Use
Intented to be use in question-answering with a context and text generation.
Out-of-Scope Use
Misuse includes any application that promotes unethical practices, misinterprets refugee law, or uses the model for malicious purposes. The model is not designed to replace professional legal advice.
Bias, Risks, and Limitations
The model, while powerful, has limitations inherent to AI, including biases present in the training data. It may not cover all nuances of refugee regulations or adapt to changes in law without updates.
Recommendations
How to Get Started with the Model
Use the code below to get started with the model.
import torch
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
BitsAndBytesConfig,
pipeline
)
model_id = "somosnlp/gemma-7b-it-legal-refugiados-es"
tokenizer_id = "somosnlp/gemma-7b-it-legal-refugiados-es"
tokenizer = AutoTokenizer.from_pretrained(tokenizer_id)
# Cargamos el modelo en 4 bits para agilizar la inferencia
quantization_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=torch.float16,
bnb_4bit_use_double_quant=True,
)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.float16,
device_map="auto",
quantization_config=quantization_config,
)
# Generamos el pipeline de generación de texto
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
# Definimos el eos token para el modelo
eos_token = tokenizer("<|im_end|>",add_special_tokens=False)["input_ids"][0]
def generate_inference(instruction, input, temperature):
prompt = pipe.tokenizer.apply_chat_template([{"role": "user",
"content": f"{instruction}/n{input}"}], tokenize=False, add_generation_prompt=True)
outputs = pipe(prompt, max_new_tokens=256, do_sample=True, num_beams=1, temperature=float(temperature), top_k=50, top_p=0.95,
max_time= 300, eos_token_id=eos_token)
return outputs[0]['generated_text'][len(prompt):].strip()
instruction = "¿Podrías explicarme brevemente los hechos que originan el procedimiento y las posibles calificaciones, así como las sanciones correspondientes, según lo expuesto en el contexto?"
input = "b) Hechos que motivan la incoación del procedimiento sucintamente expuestos, su posible calificación y las sanciones que pudieran corresponder, sin perjuicio de lo que resulte de la instrucción. c) Instructor y, en su caso, secretario del procedimiento, con expresa indicación del régimen de recusación de éstos. d) Órgano competente para la resolución del expediente y norma que le atribuye tal competencia. e) Indicación de la posibilidad de que el presunto responsable pueda reconocer voluntariamente su responsabilidad. f) Medidas de carácter provisional que se hayan acordado por el órgano competente para iniciar el procedimiento sancionador, sin perjuicio de las que se puedan adoptar durante éste de conformidad con los artículos 55 y 61 de la Ley Orgánica 4/2000, de 11 de enero. g) Indicación del derecho a formular alegaciones y a la audiencia en el procedimiento y de los plazos para su ejercicio. 2. El acuerdo de iniciación se comunicará al instructor con traslado de cuantas actuaciones existan al respecto y se notificará a los interesados, entendiéndose en todo caso por tal al expedientado. En la notificación se advertirá a los interesados que, de no efectuar alegaciones sobre el contenido de la iniciación del procedimiento en el plazo previsto en el artículo siguiente, no realizarse propuesta de prueba o no ser admitidas, por improcedentes o innecesarias, las pruebas propuestas, la iniciación podrá ser considerada propuesta de resolución cuando contenga un pronunciamiento preciso acerca de la responsabilidad imputada, con los efectos previstos en los artículos 229 y 230."
response = test_inference(instruction, input, 0.3)
print(f"Response:\n{response}")
Training Details
Training Data
The dataset used was instruct-legal-refugiados-es but we adapted the dataset to a ChatML format, described in the next section.
Training Procedure
The training was done using RTX 4090 from Vast.ai with PeRF and Lora
Preprocessing
We wanted to make a conversation model so we investigated the base model prompt in order to make conversational base on chatml format
we identified the special tokens so the model could understand the different roles in the conversation
Example
<bos><|im_start|>system
You are Gemma.<|im_end|>
<|im_start|>user
Hello, how are you?<|im_end|>
<|im_start|>assistant
I'm doing great. How can I help you today?<|im_end|>\n<eos>
So we used Phil Schmid's gemma chatml tokenizer to adapt our dataset for training
Training Hyperparameters
The following hyperparameters were used during training:
learning_rate: 5e-05
train_batch_size: 2
eval_batch_size: 8
seed: 66
gradient_accumulation_steps: 2
total_train_batch_size: 4
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: constant
lr_scheduler_warmup_ratio: 0.03
num_epochs: 3
Training regime:
Evaluation
Testing Data, Factors & Metrics
Testing Data
[More Information Needed]
Factors
[More Information Needed]
Metrics
[More Information Needed]
Results
[More Information Needed]
Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type: 1 X RTX4090
- Hours used: 4
- Cloud Provider: Vast.ai
- Compute Region: West Europe
- Carbon Emitted: 350W x 4h = 1.4 kWh x 0.57 kg eq. CO2/kWh = 0.8 kg eq. CO2
Technical Specifications
Model Architecture and Objective
The base model is google/gemma-7b finetuned in 4-bit.
Compute Infrastructure
Hardware
1 x RTX4090 GPU by Vast.ai.
Software
Libraries:
- transformers
- bitsandbytes
- accelerate
- xformers
- trl
- peft
- wandb
License
This model is under the license of the Gemma models by Google. Link to consent: https://www.kaggle.com/models/google/gemma/license/consent
Citation
BibTeX:
[More Information Needed]
@software{somosnlp2024asistenciarefugiados,
author = {Alvaro Hidalgo, Eduardo Muñoz, Teresa Martín},
title = {gemma-7b-it-legal-refugiados-es},
month = April,
year = 2024,
url = {somosnlp/gemma-7b-it-legal-refugee-v0.1.1}
}
More Information
This project was developed during the Hackathon #Somos600M organized by SomosNLP. The model was trained using GPUs sponsored by HuggingFace.
Team:
Alvaro Hidalgo Eduardo Muñoz Teresa Martin