models projecte aina FLOR 6 3B

projecte-aina-FLOR-6-3B

Overview

Model Description

FLOR-6.3B

Model description

FLOR-6.3B is a 6.3B-parameter transformer-based causal language model for Catalan, Spanish, and English. It is the result of a language adaptation technique performed on BLOOM-7.1B, which involves modifying the model's vocabulary and embedding layer, and continuously pre-training the model with 140B tokens in our target languages.

For more details, take a look at this blogpost about the project.

Intended uses and limitations

The FLOR-6.3B 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 = "Sovint em trobo pensant en tot allò que"

model_id  = "projecte-aina/FLOR-6.3B"
tokenizer = AutoTokenizer.from_pretrained(model_id)
generator = pipeline(
    "text-generation",
    model=model_id,
    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']}")

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.

Training

Language adaptation and training

The language adaptation technique used to create FLOR-6.3B requires the vocabulary of the source model to be adapted before continuing its pre-training with data in the target languages. Specifically, we proceeded as follows:

We trained our own BPE tokenizer for Catalan, Spanish, and English, and replaced the original BLOOM tokenizer and vocabulary with it. This procedure implied a downsizing of the original BLOOM's embedding layer and, therefore, a model compression from 7.1B parameters to 6.3B.
The embeddings corresponding to tokens that are present in both the original and the target vocabulary (matching tokens) were used for initialization.
The embeddings from tokens not present in BLOOM's original vocabulary were initialized as the average of all embeddings.
The model was initialized with the weights from BLOOM-7.1B, and with our adapted tokenizer (step 1) and embeddings (steps 2-3).
The model was then trained on a corpus that contains a mixture of Catalan, Spanish, and English data.

Training data

The training corpus is composed of 140B tokens gathered from web crawlings and public domain data. Most of the sources in Catalan have been obtained from the CATalog 1.0 dataset, filtered with a minimum threshold of 0.6 and oversampling some of the sources it integrates to different extents.

Dataset	Language	Words (per-epoch)	Epochs	Total Tokens
mc4	ca	5,861.79M	1.5	13,452.81M
MaCoCu	ca	1,658.89M	2	5,076.21M
CaWac	ca	1,286.83M	2.5	4,922.14M
oscar-2301	ca	1,784.57M	1.75	4,778.17M
RacoCatala Articles	ca	358.57M	4	2,194.42M
RacoCatala Forums	ca	1,301.12M	1	1,990.71M
Tesis (TDX)	ca	323.60M	4	1,980.46M
oscar-2201	ca	1,155.35M	1	1,767.69M
Wikipedia	ca	266.69M	4	1,632.17M
Nació Digital	ca	216.27M	4	1,323.59M
colossal-oscar-05-06-23	ca	207.59M	4	1,270.43M
colossal-oscar-03-04-23	ca	195.43M	4	1,196.01M
colossal-oscar-2022-27	ca	195.03M	4	1,193.59M
Crawling populars	ca	683.25M	1	1,045.38M
El Món	ca	85.27M	4	521.85M
ACN	ca	81.25M	4	497.22M
DOGV	ca	76.48M	4	468.05M
DOGC	ca	70.51M	4	431.51M
Vilaweb	ca	46.90M	4	287.04M
hplt	ca	160.27M	1	245.21M
Les Corts Valencianes	ca	26.88M	4	164.53M
IB3	ca	15.82M	4	96.82M
BOUA	ca	13.42M	4	82.13M
Parlament	ca	10.09M	4	61.77M
Aquí Berguedà	ca	8.23M	4	50.34M
Wikimedia	ca	3.90M	4	23.88M
Gutenberg	ca	1.29M	4	7.87M
OSCAR 23.01	es	53,244.56M	0.303	23,070.34M
colossal_oscar_05-06-23	es	5,548.27M	1	7,934.02M
colossal_oscar_03-04-23	es	5,090.46M	1	7,279.36M
All_bio_corpora	es	954.85M	2	2,730.88M
Wikipedia	es	777.49M	2	2,223.63M
BOE	es	1,031.28M	1	1,474.73M
Tesis (TDX)	es	268.66M	2	768.37M
Eurlex	es	459.19M	1	656.64M
CSIC	es	156.76M	2	448.33M
BORME	es	63.23M	1	90.42M
colossal_oscar_05-06-23	en	51,615.35M	0.25	21,162.30M
colossal_oscar_03-04-23	en	49,454.01M	0.14	11,354.64M
Wikipedia	en	2,116.53M	2	6,942.23M
Gutenberg	en	3,513.82M	1	5,762.66M
Eurlex	en	438.92M	1	719.83M
legal-mc4	en	417.97M	1	685.47M

Languages

The training data has the same amount of Catalan, Spanish, and English texts. The table below shows the final language distribution:

Language	Percentage
Catalan (CA)	33.39%
Spanish (ES)	33.32%
English (EN)	33.29%

Framework

The training was conducted in 16 Cerebras' CS-2 systems using the cs-2.0.2 release of their software.

Evaluation

FLOR-6.3B has been evaluated in a 5-shot setting, using EleutherAI's LM Evaluation Harness. The evaluation benchmark includes tasks in Catalan, Spanish, and English, with particular emphasis on Catalan datasets.

The tasks were chosen to cover several evaluation areas in order to provide a comprehensive overview of the model's capabilities. The baselines used to compare our results are multilingual and English open-source 7B models and smaller models of the FLOR family of models: TBC.

Our implementation of EleutherAI's LM Evaluation Harness can be found here.

The following is a list of evaluation areas and their respective datasets:

Reading Comprehension: Belebele
Question Answering: XQuAD, CatalanQA, CoQCat
Natural Language Inference: XNLI and its translation to Catalan (XNLI-ca), TE-ca
Paraphrase Identification: PAWS-X and its translation to Catalan (PAWS-ca), Parafraseja
Commonsense Reasoning: COPA and its translation to Catalan (COPA-ca)
Translation: Flores-200

Results

Dataset	Lang.	Task	FLOR-6.3B	BLOOM-7.1B
Teca	ca	Natural Language Inference	49.79🔥	46.91
XNLI	ca	Natural Language Inference	51.70🔥	49.20
XNLI	es	Natural Language Inference	50.28🔥	47.62
XNLI	en	Natural Language Inference	52.55🔥	51.96
Belebele	ca	Reading Comprehension	48.98🔥	48.57
Belebele	es	Reading Comprehension	48.16	48.16
Belebele	en	Reading Comprehension	49.80	50.20🔥
CatalanQA	ca	Question Answering	71.80🔥	69.54
CoQCat	ca	Question Answering	65.96🔥	58.49
XQuAD	ca	Question Answering	59.01	60.94🔥
XQuAD	es	Question Answering	63.80🔥	61.76
XQuAD	en	Question Answering	70.02🔥	69.76
COPA	ca	Question Answering	78.00🔥	72.60
COPA	en	Question Answering	81.00🔥	79.00
XStoryCloze	es	Question Answering	69.82🔥	66.45
XStoryCloze	en	Question Answering	74.45🔥	70.81
Parafraseja	ca	Paraphrase Identification	62.88🔥	60.27
PAWS-X	ca	Paraphrase Identification	59.70🔥	59.35
PAWS-X	es	Paraphrase Identification	57.70	58.65🔥
PAWS-X	en	Paraphrase Identification	59.65	62.85🔥
FLoRes	ca->es	Machine Translation	24.98🔥	24.21
FLoRes	es->ca	Machine Translation	25.24🔥	23.19
FLoRes	ca->en	Machine Translation	42.89🔥	40.93
FLoRes	en->ca	Machine Translation	39.29🔥	34.30
FLoRes	es->en	Machine Translation	28.61🔥	27.48
FLoRes	en->es	Machine Translation	25.35🔥	23.72

Note: The metrics are F1-score for question-answering tasks, BLEU for translation, and accuracy for the rest.

Additional information

Author

The Language Technologies Unit from Barcelona Supercomputing Center.

Contact

For further information, please send an email to [email protected].

Copyright

License

Apache License, Version 2.0

Funding

This work was funded by [Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya](https://politiquesdigitals.gencat.cat/ca/inici/index.html#googtrans(ca|en) within the framework of Projecte AINA.

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.

Inference samples

Inference type	Python sample (Notebook)	CLI with YAML
Real time	text-generation-online-endpoint.ipynb	text-generation-online-endpoint.sh
Batch	text-generation-batch-endpoint.ipynb	coming soon

Sample inputs and outputs

Sample input

{
  "input_data": {
    "input_string": [
      "Once upon a time,"
    ],
    "parameters": {
      "top_p": 0.8,
      "temperature": 0.8,
      "max_new_tokens": 90,
      "do_sample": true
    }
  }
}

Sample output

[
  {
    "0": "Once upon a time, there was a village where the villagers lived in peace and harmony. They worked together, shared their food and resources, and lived in a way that made them happy.\n\nOne day, a stranger arrived in the village. He was a wise and powerful man who could see the future. He told the villagers that their way of life was not sustainable and that they needed to change it.\n\nThe villa"
  }
]

Version: 1

Wiki menu

Home
Reference Documentation
- Components
- Data
- Environments
- Models
Contributing

Provide feedback

Saved searches

Use saved searches to filter your results more quickly