SMC / README.md
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metadata
language:
  - es
  - en
license: apache-2.0
dataset_info:
  features:
    - name: raw_text
      dtype: string
    - name: topic
      dtype: string
    - name: speciallity
      dtype: string
    - name: raw_text_type
      dtype: string
    - name: topic_type
      dtype: string
    - name: source
      dtype: string
    - name: country
      dtype: string
    - name: document_id
      dtype: string
  splits:
    - name: train
      num_bytes: 190710909
      num_examples: 2136490
  download_size: 48472707
  dataset_size: 190710909
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
task_categories:
  - question-answering
  - zero-shot-classification
  - text-generation
pretty_name: SMC

Dataset Card for Spanish Medical Corpus (SMC)

This dataset groups and organizes several datasets present in hugginface (e.g.: PlanTL-GOB-ES/cantemist-ner, PlanTL-GOB-ES/pharmaconer) and other public resources created by researchers with different formats (e.g.; MedLexSp ) to allow it to be a source of knowledge of large language models in Spanish for the medical domain.

Dataset Card in Spanish

Dataset Details

Dataset Description

Dataset Sources

Uses

The use of this dataset is suggested to achieve self-tuning and pre-training of LLM for the medical domain with information in Spanish.

Direct Use

Fine Tuning an LLM instruction in Spanish language with question prompts and answers.

Out-of-Scope Use

The creators of the dataset are not responsible for harmful results that the models may generate when trained with this information. A rigorous evaluation process with specialists of the results generated by trained LLM models is suggested.

Dataset Structure

For each entry or document in the information source, organize it in a Hugginface dataset as follows:

  • question (raw_text): Text associated with the document, question, clinical case or other type of information.
  • answer (topic): (Text associated with medical treatment (healthcare_treatment), diagnosis (healthcare_diagnosis), health topic (topic), answer to a question (answer), other, or be empty e.g. in the open text)
  • speciality: (Medical specialty to which the raw_text relates, e.g. cardiology, surgery, others)
  • raw_text_type: (Can be clinic_case, open_text, question or empty)
  • topic_type: (It can be medical topic, medical diagnosis, answer, natural medicine topic, other, or empty)
  • source: Identifier of the source associated with the document that appears in the README and description of the dataset.
  • country: Identifier of the country of origin of the source (e.g.; ch, es) using the ISO 3166-1 alpha-2 standard (Two-letter country codes).
  • document_id: Document identifier in the source dataset, this value can be empty in case it is not known.

At the beginning of this construction process, the table in the Source Data section must be updated. description of the source of information with the following data:

  • Id: This will be a number so that the source of information can be referenced in each entry of the data set.
  • Name: Name of the source from which it comes.
  • Tokens: Number of tokens it contains.
  • Memory: Memory size of the dataset generated for huggingface
  • Licencia: In this case, if it is only for research or if you have another license such as MIT, Apache 2 or others
  • Address: URL from where the information can be downloaded or consulted.
  • Country: Information source country of the using the ISO 3166-1 standard alpha-2 code: 2-letter ISO code assigned to that country or territory.

Dataset Creation

Curation Rationale

More than 600 million Spanish speakers need resources, such as LLMs, to obtain medical information freely and safe, complying with the millennium objectives: Health and Wellbeing, Education and Quality, End of Poverty proposed by the UN. There are few resources or data sets from the medical domain for training or self-tuning for an LLM in the Spanish language.

To train an LLM in the domain of medicine and healthcare, a large amount of data from this context is needed. To create a data set in the medical domain, some certification by specialists in corpus construction is necessary.

Source Data

Id Name Tokens Memory Licencia Address Country
1 Cantemist corpus: gold standard of oncology clinical cases annotated with CIE-O 3 terminology 349287 9157 kB CC Attribution 4.0 International https://huggingface.co/datasets/bigbio/cantemist/viewer/cantemist_bigbio_kb es
2 MedlinePlus Spanish (National Library of Medicine, NLM) 7757337 35 MB https://medlineplus.gov/spanish/ es
3 PharmaCoNER 275955 2 MB CC Attribution 4.0 International https://huggingface.co/datasets/PlanTL-GOB-ES/pharmaconer es
4 Spanish Biomedical Crawled Corpus 1973048 264 MB cc-by-4.0 https://zenodo.org/records/5513237 es
5 CARES 322353 1828 kB Afl-3.0 https://huggingface.co/datasets/chizhikchi/CARES es
6 MEDDOCAN 364462 1639 kB cc-by-4.0 https://huggingface.co/datasets/bigbio/meddocan es
7 Alvaro8gb/enfermedades-wiki-marzo-2024 1424685 9073 kB MIT https://huggingface.co/datasets/Alvaro8gb/enfermedades-wiki-marzo-2024 es
8 BioMistral/BioInstructQA(spanish) 1072476 5963 kB Apache 2.0 https://huggingface.co/datasets/BioMistral/BioInstructQA ca
9 DisTEMIST 550203 2754 kB cc-by-4.0 https://huggingface.co/datasets/bigbio/distemist es
10 The Chilean Waiting List Corpus 678934 3116 kB cc-by-4.0 https://zenodo.org/records/5518225 or https://huggingface.co/plncmm cl
11 BARR2 1732432 8472 kB cc-by-4.0 https://temu.bsc.es/BARR2/downloads/background_set.raw_text.tar.bz2 es
12 SPACC 551849 2711 kB cc-by-4.0 https://zenodo.org/records/2560316 es
13 MedLexSp 608374 21 MByte MedLexSp is distributed freely for research or educational purposes. You need to sign an agreement with the authors for other purposes. https://digital.csic.es/handle/10261/270429 es

Data Collection and Processing

Sugerencias:

Who are the source data producers?

Different events, NLP competitions or the construction of data sets for LLM such as BioMistral. See table in Source Data section

Annotation process

The annotation process was automatic, converting the data sources to the attributes of the new data set.

Who are the annotators?

See the section Team

Personal and Sensitive Information

In the construction process, it was taken into account that sensitive user data was not included in any of the cases (e.g., clinical cases).

Bias, Risks, and Limitations

It is suggested to take into account the scope of the license of each of the sources (e.g., review the source and License field in the previous table).

If you need to filter by data source or other criteria, you can use the properties of the Dataset data structure of the framework. Hugginface. In the following code example, the entries that have a topic type about medical diagnosis or a medical topic are obtained from the data set:


  spanishMedicaLllmDataset =
      load_dataset(SPANISH_MEDICA_LLM_DATASET, split="train")
      
  spanishMedicaLllmDataset =
      spanishMedicaLllmDataset.filter(lambda example: example["topic_type"] in ['medical_diagnostic' | 'medical_topic'])

Recommendations

Personnel using this dataset must be aware of the risks, biases and limitations of the dataset.

For the autotuning of an LLM, it is suggested to take into account the rows where the topic type (ed., topic_type field) has values: medical_topic, medical_diagnostic, answer, natural_medicine_topic. Because it indicates that this field is not empty and has value for the creation of instructions of the question and answer form.

For LLM pre-training, it is suggested to take into account when the raw_text_type field is equal to open_text. This indicates that the text is not part of a question/answer format but has important value for LLM pre-training.

License

Apache License 2.0

Citation

BibTeX:

@software{lopez2024spanishmedicallm,
  author = {Lopez Dionis, Garcia Alvaro, Montoya Dylan, Bermúdez Daniel},
  title = {SpanishMedicaLLM},
  month = February,
  year = 2024,
  url = {https://huggingface.co/datasets/somosnlp/SMC}
}

More Information

This project was developed during the Hackathon #Somos600M organized by SomosNLP. The dataset was created using distilabel by Argilla and endpoints sponsored by HuggingFace.

Team:

Contact

For any doubt or suggestion contact to: PhD Dionis López (inoid2007@gmail.com)