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README.md

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+---
+language: 
+  - es
+tags:
+- biomedical
+- clinical
+- spanish
+license: apache-2.0
+metrics:
+- ppl
+widget:
+- text: "El único antecedente personal a reseñar era la <mask> arterial."
+- text: "Las radiologías óseas de cuerpo entero no detectan alteraciones <mask>, ni alteraciones vertebrales."
+- text: "En el <mask> toraco-abdómino-pélvico no se encontraron hallazgos patológicos de interés."
+---
+
+# Biomedical-clinical language model for Spanish
+Biomedical pretrained language model for Spanish. For more details about the corpus, the pretraining and the evaluation, check the official [repository](https://github.com/PlanTL-SANIDAD/lm-biomedical-clinical-es) and read our [preprint](https://arxiv.org/abs/2109.03570) "_Carrino, C. P., Armengol-Estapé, J., Gutiérrez-Fandiño, A., Llop-Palao, J., Pàmies, M., Gonzalez-Agirre, A., & Villegas, M. (2021). Biomedical and Clinical Language Models for Spanish: On the Benefits of Domain-Specific Pretraining in a Mid-Resource Scenario._".
+
+## Tokenization and model pretraining
+This model is a [RoBERTa-based](https://github.com/pytorch/fairseq/tree/master/examples/roberta) model trained on a
+**biomedical-clinical** corpus in Spanish collected from several sources (see next section). 
+The training corpus has been tokenized using a byte version of [Byte-Pair Encoding (BPE)](https://github.com/openai/gpt-2)
+used in the original [RoBERTA](https://github.com/pytorch/fairseq/tree/master/examples/roberta) model with a vocabulary size of 52,000 tokens. The pretraining consists of a masked language model training at the subword level following the approach employed for the RoBERTa base model with the same hyperparameters as in the original work. The training lasted a total of 48 hours with 16 NVIDIA V100 GPUs of 16GB DDRAM, using Adam optimizer with a peak learning rate of 0.0005 and an effective batch size of 2,048 sentences.
+
+## Training corpora and preprocessing
+
+The training corpus is composed of several biomedical corpora in Spanish, collected from publicly available corpora and crawlers, and a real-world clinical corpus collected from more than 278K clinical documents and notes. To obtain a high-quality training corpus while retaining the idiosyncrasies of the clinical language, a cleaning pipeline has been applied only to the biomedical corpora, keeping the clinical corpus uncleaned. Essentially, the cleaning operations used are:
+
+- data parsing in different formats
+  - sentence splitting
+  - language detection
+  - filtering of ill-formed sentences 
+  - deduplication of repetitive contents
+  - keep the original document boundaries
+
+Then, the biomedical corpora are concatenated and further global deduplication among the biomedical corpora have been applied.
+Eventually, the clinical corpus is concatenated to the cleaned biomedical corpus resulting in a medium-size biomedical-clinical corpus for Spanish composed of more than 1B tokens. The table below shows some basic statistics of the individual cleaned corpora:
+
+    
+| Name                                                                                    | No. tokens  | Description                                                                                                                                                                                                                                          |
+|-----------------------------------------------------------------------------------------|-------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| [Medical crawler](https://zenodo.org/record/4561970)                                    | 745,705,946 | Crawler of more than 3,000 URLs belonging to Spanish biomedical and health domains.                                                                                                                                                                                 |
+| Clinical cases misc.                                                                    | 102,855,267 | A miscellany of medical content, essentially clinical cases. Note that a clinical case report is a scientific publication where medical practitioners share patient cases and it is different from a clinical note or document.                                                                                                                                                                                 |
+| Clinical notes/documents                                                                | 91,250,080 | Collection of more than 278K clinical documents, including discharge reports, clinical course notes and X-ray reports, for a total of 91M tokens.                                                                                                                                                                                 |
+| [Scielo](https://github.com/PlanTL-SANIDAD/SciELO-Spain-Crawler)                        | 60,007,289  | Publications written in Spanish crawled from the Spanish SciELO server in 2017.                                                                                                                                       |
+| [BARR2_background](https://temu.bsc.es/BARR2/downloads/background_set.raw_text.tar.bz2) | 24,516,442  | Biomedical Abbreviation Recognition and Resolution (BARR2) containing Spanish clinical case study sections from a variety of clinical disciplines.                                                                                       |
+| Wikipedia_life_sciences                                                                 | 13,890,501  | Wikipedia articles crawled 04/01/2021 with the [Wikipedia API python library](https://pypi.org/project/Wikipedia-API/) starting from the "Ciencias\_de\_la\_vida" category up to a maximum of 5 subcategories. Multiple links to the same articles are then discarded to avoid repeating content.                                                                                                                                                                    |
+| Patents                                                                                 | 13,463,387  | Google Patent in Medical Domain for Spain (Spanish). The accepted codes (Medical Domain) for Json files of patents are: "A61B", "A61C","A61F", "A61H", "A61K", "A61L","A61M", "A61B", "A61P".                                                        |
+| [EMEA](http://opus.nlpl.eu/download.php?f=EMEA/v3/moses/en-es.txt.zip)                  | 5,377,448   | Spanish-side documents extracted from parallel corpora made out of PDF documents from the European Medicines Agency.                                                                                                                            |
+| [mespen_Medline](https://zenodo.org/record/3562536#.YTt1fH2xXbR)                        | 4,166,077   | Spanish-side articles extracted from a collection of Spanish-English parallel corpus consisting of biomedical scientific literature.  The collection of parallel resources are aggregated from the MedlinePlus source. |
+| PubMed                                                                                  | 1,858,966   | Open-access articles from the PubMed repository crawled in 2017.                                                                                                                                              |
+
+
+
+## Evaluation and results
+
+The model has been evaluated on the Named Entity Recognition (NER) using the following datasets:
+
+ - [PharmaCoNER](https://zenodo.org/record/4270158): is a track on chemical and drug mention recognition from Spanish medical texts (for more info see: https://temu.bsc.es/pharmaconer/).
+
+ - [CANTEMIST](https://zenodo.org/record/3978041#.YTt5qH2xXbQ): is a shared task specifically focusing on named entity recognition of tumor morphology, in Spanish (for more info see: https://zenodo.org/record/3978041#.YTt5qH2xXbQ). 
+
+ - ICTUSnet: consists of 1,006 hospital discharge reports of patients admitted for stroke from 18 different Spanish hospitals. It contains more than 79,000 annotations for 51 different kinds of variables.
+
+The evaluation results are compared against the [mBERT](https://huggingface.co/bert-base-multilingual-cased) and [BETO](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) models:
+
+| F1 - Precision - Recall | roberta-base-biomedical-clinical-es | mBERT                   | BETO                    |
+|---------------------------|----------------------------|-------------------------------|-------------------------|
+| PharmaCoNER               | **90.04** - **88.92** - **91.18**    | 87.46 - 86.50 - 88.46 | 88.18 - 87.12 - 89.28 |
+| CANTEMIST                 | **83.34** - **81.48** - **85.30**    | 82.61 - 81.12 - 84.15 | 82.42 - 80.91 - 84.00 |
+| ICTUSnet                  | **88.08** - **84.92** - **91.50**    | 86.75 - 83.53 - 90.23 | 85.95 - 83.10 - 89.02 |
+
+
+## Intended uses & limitations
+
+The model is ready-to-use only for masked language modelling to perform the Fill Mask task (try the inference API or read the next section)
+
+However, the is intended to be fine-tuned on downstream tasks such as Named Entity Recognition or Text Classification.
+
+## Cite
+If you use our models, please cite our latest preprint:
+
+```bibtex
+
+@misc{carrino2021biomedical,
+      title={Biomedical and Clinical Language Models for Spanish: On the Benefits of Domain-Specific Pretraining in a Mid-Resource Scenario}, 
+      author={Casimiro Pio Carrino and Jordi Armengol-Estapé and Asier Gutiérrez-Fandiño and Joan Llop-Palao and Marc Pàmies and Aitor Gonzalez-Agirre and Marta Villegas},
+      year={2021},
+      eprint={2109.03570},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+
+```
+
+If you use our Medical Crawler corpus, please cite the preprint:
+
+```bibtex
+
+@misc{carrino2021spanish,
+      title={Spanish Biomedical Crawled Corpus: A Large, Diverse Dataset for Spanish Biomedical Language Models}, 
+      author={Casimiro Pio Carrino and Jordi Armengol-Estapé and Ona de Gibert Bonet and Asier Gutiérrez-Fandiño and Aitor Gonzalez-Agirre and Martin Krallinger and Marta Villegas},
+      year={2021},
+      eprint={2109.07765},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+
+```
+
+---
+
+---
+
+## How to use
+
+```python
+from transformers import AutoTokenizer, AutoModelForMaskedLM
+
+tokenizer = AutoTokenizer.from_pretrained("BSC-TeMU/roberta-base-biomedical-es")
+
+model = AutoModelForMaskedLM.from_pretrained("BSC-TeMU/roberta-base-biomedical-es")
+
+from transformers import pipeline
+
+unmasker = pipeline('fill-mask', model="BSC-TeMU/roberta-base-biomedical-es")
+
+unmasker("El único antecedente personal a reseñar era la <mask> arterial.")
+```
+```
+# Output
+[
+  {
+    "sequence": " El único antecedente personal a reseñar era la hipertensión arterial.",
+    "score": 0.9855039715766907,
+    "token": 3529,
+    "token_str": " hipertensión"
+  },
+  {
+    "sequence": " El único antecedente personal a reseñar era la diabetes arterial.",
+    "score": 0.0039140828885138035,
+    "token": 1945,
+    "token_str": " diabetes"
+  },
+  {
+    "sequence": " El único antecedente personal a reseñar era la hipotensión arterial.",
+    "score": 0.002484665485098958,
+    "token": 11483,
+    "token_str": " hipotensión"
+  },
+  {
+    "sequence": " El único antecedente personal a reseñar era la Hipertensión arterial.",
+    "score": 0.0023484621196985245,
+    "token": 12238,
+    "token_str": " Hipertensión"
+  },
+  {
+    "sequence": " El único antecedente personal a reseñar era la presión arterial.",
+    "score": 0.0008009297889657319,
+    "token": 2267,
+    "token_str": " presión"
+  }
+]
+```

args.json

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+{
+    "custom_vocab_files": [
+        "/home/usuaris/veu/casimiro.pio.carrino/projects/corpus-utils-lm/corpora/bio/biomedical-clinical.txt"
+    ],
+    "vocab_name": "bio-biomedical-clinical-vocab-52k",
+    "tokenizer": "bbpe",
+    "lowercase": false,
+    "vocab_size": 52000,
+    "min_frequency": 10,
+    "extra_tokens": [],
+    "limit_alphabet": 1000,
+    "no_show_progress": false,
+    "strip_accents": false,
+    "no_handle_chinese_chars": false,
+    "no_clean_text": false,
+    "reserve_tokens": 0,
+    "use_tokenizers": false,
+    "no_fairseq": false,
+    "files": [
+        "/home/usuaris/veu/casimiro.pio.carrino/projects/corpus-utils-lm/corpora/bio/biomedical-clinical.txt"
+    ],
+    "output_root_path": "/home/usuaris/veu/casimiro.pio.carrino/projects/corpus-utils-lm/output/model-ready_output/bio-biomedical-clinical-vocab-52k-2021-04-26-0955-3a71-240f",
+    "commit_hash": "3a7116cf776527c411869becbe6fad8b9e3f5e56"
+}

config.json

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+{
+  "architectures": [
+    "RobertaForMaskedLM"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "eos_token_id": 2,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "roberta",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "transformers_version": "4.4.0",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 52000
+}

process.log

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+Executing train_tokenizer.py
+------------------------------
+training bbpe tokenizer
+Initialize an empty tokenizer
+training
+saving model tokenizer to /home/usuaris/veu/casimiro.pio.carrino/projects/corpus-utils-lm/output/model-ready_output/bio-biomedical-clinical-vocab-52k-2021-04-26-0955-3a71-240f/train_tokenizer_output/train-tokenizer-2021-04-26-1009-3a71-e9ca
+saving pretrained to /home/usuaris/veu/casimiro.pio.carrino/projects/corpus-utils-lm/output/model-ready_output/bio-biomedical-clinical-vocab-52k-2021-04-26-0955-3a71-240f/train_tokenizer_output/train-tokenizer-2021-04-26-1009-3a71-e9ca
+saving config to /home/usuaris/veu/casimiro.pio.carrino/projects/corpus-utils-lm/output/model-ready_output/bio-biomedical-clinical-vocab-52k-2021-04-26-0955-3a71-240f/train_tokenizer_output/train-tokenizer-2021-04-26-1009-3a71-e9ca

pytorch_model.bin

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+oid sha256:2e0c8d7bb348e40327b7c38eb6995c74d5c64345bc4ab9b3deff58e7359f15f7
+size 504420627

special_tokens_map.json

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+{"bos_token": {"content": "<s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, "eos_token": {"content": "</s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, "unk_token": {"content": "<unk>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, "sep_token": {"content": "</s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, "pad_token": {"content": "<pad>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, "cls_token": {"content": "<s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, "mask_token": {"content": "<mask>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true}}

tokenizer_config.json

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+{"unk_token": {"content": "<unk>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "bos_token": {"content": "<s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "eos_token": {"content": "</s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "add_prefix_space": true, "errors": "replace", "sep_token": {"content": "</s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "cls_token": {"content": "<s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "pad_token": {"content": "<pad>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "mask_token": {"content": "<mask>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "max_len": 512, "special_tokens_map_file": null, "name_or_path": "/home/usuaris/veu/casimiro.pio.carrino/projects/corpus-utils-lm/output/model-ready_output/bio-biomedical-clinical-vocab-52k-2021-04-26-0955-3a71-240f/train_tokenizer_output/train-tokenizer-2021-04-26-1009-3a71-e9ca"}