Speedy Gonzales: A Collection of Fast Task-Specific Models for Spanish

Summary

Model	Parameters	Speedup	Score
Fine-tuning
BETO uncased	110M	1.00x	81.02
BETO cased	110M	1.00x	84.82
DistilBETO	67M	2.00x	76.73
ALBETO tiny	5M	18.05x	74.97
ALBETO base	12M	0.99x	83.25
ALBETO large	18M	0.28x	82.02
ALBETO xlarge	59M	0.07x	84.13
ALBETO xxlarge	223M	0.03x	85.17
BERTIN	125M	1.00x	83.97
RoBERTa BNE base	125M	1.00x	84.83
RoBERTa BNE large	355M	0.28x	68.42
Task-specific Knowledge Distillation
ALBETO tiny	5M	18.05x	76.49
ALBETO base-2	12M	5.96x	72.98
ALBETO base-4	12M	2.99x	80.06
ALBETO base-6	12M	1.99x	82.70
ALBETO base-8	12M	1.49x	83.78
ALBETO base-10	12M	1.19x	84.32

All results

Model	Text Classification (Accuracy)			Sequence Tagging (F1 Score)		Question Answering (F1 Score / Exact Match)
	MLDoc	PAWS-X	XNLI	POS	NER	MLQA	SQAC	TAR / XQuAD
Fine-tuning
BETO uncased	96.38	84.25	77.76	97.81	80.85	64.12 / 40.83	72.22 / 53.45	74.81 / 54.62
BETO cased	96.65	89.80	81.98	98.95	87.14	67.65 / 43.38	78.65 / 60.94	77.81 / 56.97
DistilBETO	96.35	75.80	76.59	97.67	78.13	57.97 / 35.50	64.41 / 45.34	66.97 / 46.55
ALBETO tiny	95.82	80.20	73.43	97.34	75.42	51.84 / 28.28	59.28 / 39.16	66.43 / 45.71
ALBETO base	96.07	87.95	79.88	98.21	82.89	66.12 / 41.10	77.71 / 59.84	77.18 / 57.05
ALBETO large	92.22	86.05	78.94	97.98	82.36	65.56 / 40.98	76.36 / 56.54	76.72 / 56.21
ALBETO xlarge	95.70	89.05	81.68	98.20	81.42	68.26 / 43.76	78.64 / 59.26	80.15 / 59.66
ALBETO xxlarge	96.85	89.85	82.42	98.43	83.06	70.17 / 45.99	81.49 / 62.67	79.13 / 58.40
BERTIN	96.47	88.65	80.50	99.02	85.66	66.06 / 42.16	78.42 / 60.05	77.05 / 57.14
RoBERTa BNE base	96.82	89.90	81.12	99.00	86.80	67.31 / 44.50	80.53 / 62.72	77.16 / 55.46
RoBERTa BNE large	97.00	90.00	51.62	61.83	21.47	67.69 / 44.88	80.41 / 62.14	77.34 / 56.97
Task-specific Knowledge Distillation
ALBETO tiny	96.40	85.05	75.99	97.36	72.51	54.17 / 32.22	63.03 / 43.35	67.47 / 46.13
ALBETO base-2	96.20	76.75	73.65	97.17	69.69	48.62 / 26.17	58.40 / 39.00	63.41 / 42.35
ALBETO base-4	96.35	86.40	78.68	97.60	74.58	62.19 / 38.28	71.41 / 52.87	73.31 / 52.43
ALBETO base-6	96.40	88.45	81.66	97.82	78.41	66.35 / 42.01	76.99 / 59.00	75.59 / 56.72
ALBETO base-8	96.70	89.75	82.55	97.96	80.23	67.39 / 42.94	77.79 / 59.63	77.89 / 56.72
ALBETO base-10	96.88	89.95	82.26	98.00	81.10	68.29 / 44.29	79.89 / 62.04	78.21 / 56.21

Citation

Speedy Gonzales: A Collection of Fast Task-Specific Models for Spanish

To cite this resource in a publication please use the following:

@inproceedings{canete-bravo-marquez-2024-speedy,
    title = "Speedy Gonzales: A Collection of Fast Task-Specific Models for {S}panish",
    author = "Ca{\~n}ete, Jos{\'e}  and
      Bravo-Marquez, Felipe",
    editor = "Bollegala, Danushka  and
      Shwartz, Vered",
    booktitle = "Proceedings of the 13th Joint Conference on Lexical and Computational Semantics (*SEM 2024)",
    month = jun,
    year = "2024",
    address = "Mexico City, Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.starsem-1.14",
    pages = "176--189",
    abstract = "Large language models (LLM) are now a very common and successful path to approach language and retrieval tasks. While these LLM achieve surprisingly good results it is a challenge to use them on more constrained resources. Techniques to compress these LLM into smaller and faster models have emerged for English or Multilingual settings, but it is still a challenge for other languages. In fact, Spanish is the second language with most native speakers but lacks of these kind of resources. In this work, we evaluate all the models publicly available for Spanish on a set of 6 tasks and then, by leveraging on Knowledge Distillation, we present Speedy Gonzales, a collection of inference-efficient task-specific language models based on the ALBERT architecture. All of our models (fine-tuned and distilled) are publicly available on: https://huggingface.co/dccuchile.",
}