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FLOR-1.3B

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Model description

FLOR-1.3B is a 1.3B-parameter transformer-based causal language model for Catalan, Spanish, and English. It is the result of a language adaptation technique performed on BLOOM-1.7B, which involves modifying the model's vocabulary and embedding layer, and continuously pre-training the model with 26B tokens in our target languages.

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

Intended uses and limitations

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

model_id  = "projecte-aina/FLOR-1.3B"
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-1.3B 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:

  1. 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 1.7B parameters to 1.3B.
  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 BLOOM's original vocabulary were initialized as the average of all embeddings.
  4. The model was initialized with the weights from BOOM-1.7B, and with our adapted tokenizer (step 1) and embeddings (steps 2-3).
  5. The model was then trained on a corpus that contains a mixture of Catalan, Spanish, and English data.

Training data

The training corpus is the same that was used to train Ǎguila-7B. It consists of 26B tokens of several corpora gathered from web crawlings and public domain data.

Dataset Language Words (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
RacoCatalà Noticias ca 17.16M 2.142216727
RacoCatalà Forums ca 333.73M 2.142216727
CaWaC ca 57.79M 2.142216727
Wikipedia ca 228.01M 3.570361212
Vilaweb ca 50.34M 2.142216727

Languages

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

Language Percentage
English (EN) 16.84%
Spanish (ES) 41.38%
Catalan (CA) 41.79%

Training hyperparameters

  • seed: 1
  • distributed_type: WSE-2
  • num_devices: 1
  • train_batch_size: 60
  • eval_batch_size: 60
  • optimizer: AdamW
  • betas: (0.9,0.95)
  • epsilon: 1e-08
  • weight_decay_rate: 0.1
  • learning_rate:
    • scheduler: "Linear"
      initial_learning_rate: 0.0
      end_learning_rate: 4.1e-5
      steps: 3050
    • scheduler: "CosineDecay"
      initial_learning_rate: 4.1e-5
      end_learning_rate: 3.4e-6
      steps: 209133
    • scheduler: "Constant"
      learning_rate: 2.2e-6
  • num_epochs: 1.0

Framework

The training was conducted in a Cerebras' CS-2 system using the cs-1.9.1 release of their software.

Evaluation

FLOR-1.3B 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 1.3B models: mGPT-1.3B, GPT-Neo-1.3B, Pythia-1.4B, OPT-1.3B, Falcon-rw-1.3B, and Cerebras-GPT-1.3B.

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 and Questions Answering

Model Belebele-ca Belebele-es Belebele-en XQuAD-ca XQuAD-es XQuAD-en CatalanQA CoQCat
Random 25.00 25.00 25.00 - - - - -
mGPT-1.3B 26.64 25.82 28.07 0.33 0.67 0.17 0.65 0.78
GPT-Neo-1.3B 39.55 37.50 42.83 19.75 29.77 51.53 22.34 23.57
Pythia-1.4B 38.32 36.89 44.26 26.19 34.13 52.98 27.47 25.38
OPT-1.3B 35.86 37.09 45.49 23.53 31.85 52.95 26.58 20.18
Falcon-rw-1.3B 34.84 35.66 50.61 5.93 19.25 58.60 6.91 15.61
Cerebras-GPT-1.3B 32.79 31.76 35.04 8.56 19.98 36.00 10.87 14.12
BLOOM-1.1B 39.34 38.32 41.19 36.81 36.98 44.10 44.65 34.57
FLOR-1.3B 43.85 38.11 40.57 43.52 44.31 44.11 54.25 48.15

Natural Language Inference and Paraphrase Identification

Model XNLI-ca XNLI-es XNLI-en TECA-ca PAWS-X-ca PAWS-X-es PAWS-X-en Parafraseja
Random 33.33 33.33 33.33 33.33 50.00 50.00 50.00 50.00
mGPT-1.3B 40.06 43.81 45.67 37.03 51.00 52.30 56.15 51.32
GPT-Neo-1.3B 41.44 45.57 49.92 35.38 54.65 53.40 54.60 51.70
Pythia-1.4B 42.46 45.61 51.00 37.46 54.15 52.50 57.70 55.23
OPT-1.3B 40.08 44.53 52.48 36.14 54.10 52.55 55.90 53.23
Falcon-rw-1.3B 34.53 35.85 45.73 34.96 54.25 54.05 53.65 50.60
Cerebras-GPT-1.3B 36.83 38.88 47.25 35.62 52.40 52.20 55.95 52.05
BLOOM-1.1B 47.19 46.39 49.44 41.38 55.05 54.05 54.75 55.65
FLOR-1.3B 49.20 48.82 47.45 42.89 53.20 52.85 53.00 57.43

Commonsense Reasoning and Translation

Model XStoryCloze-es XStoryCloze-en COPA-ca COPA-en FloRes (ca->es) FloRes (es->ca) FloRes (ca->en) FloRes (en->ca) FloRes (es->en) FloRes (en->es)
Random 50.00 50.00 50.00 50.00 - - - - - -
mGPT-1.3B 55.33 60.09 52.20 63.40 3.25 2.96 9.25 3.79 17.75 15.34
GPT-Neo-1.3B 51.42 66.58 53.40 74.80 3.27 3.80 17.77 5.49 17.70 12.04
Pythia-1.4B 54.14 68.37 52.20 78.60 9.68 5.74 24.03 11.10 21.50 15.04
OPT-1.3B 53.94 69.95 52.60 76.20 3.14 3.52 15.39 2.00 16.33 6.53
Falcon-rw-1.3B 51.09 71.34 52.40 79.60 3.03 3.59 8.89 3.01 14.17 6.50
Cerebras-GPT-1.3B 49.11 60.62 51.40 66.80 2.42 1.81 2.69 0.82 3.36 1.77
BLOOM-1.1B 57.91 62.48 62.80 66.40 21.62 15.28 31.16 21.28 20.92 16.84
FLOR-1.3B 64.06 61.81 68.00 67.80 22.16 18.58 33.95 29.31 23.09 20.30

Additional information

Author

The Language Technologies Unit from Barcelona Supercomputing Center.

Contact

For further information, please send an email to langtech@bsc.es.

Copyright

Copyright(c) 2023 by Language Technologies Unit, Barcelona Supercomputing Center.

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 within the framework of Projecte AINA.

Disclaimer

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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.

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