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Files changed (10) hide show
  1. .gitattributes +1 -0
  2. README.md +8 -9
  3. args.json +10 -14
  4. config.json +2 -2
  5. dict.txt +1738 -0
  6. merges.txt +0 -0
  7. process.log +3 -3
  8. pytorch_model.bin +2 -2
  9. tokenizer_config.json +1 -1
  10. vocab.json +0 -0
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@@ -25,3 +25,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zstandard filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ pytorch_model.bin filter=lfs diff=lfs merge=lfs -text
README.md CHANGED
@@ -14,8 +14,10 @@ widget:
14
  - text: "En el <mask> toraco-abdómino-pélvico no se encontraron hallazgos patológicos de interés."
15
  ---
16
 
 
 
17
  # Biomedical-clinical language model for Spanish
18
- 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-GOB-ES/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._".
19
 
20
  ## Tokenization and model pretraining
21
  This model is a [RoBERTa-based](https://github.com/pytorch/fairseq/tree/master/examples/roberta) model trained on a
@@ -43,7 +45,7 @@ Eventually, the clinical corpus is concatenated to the cleaned biomedical corpus
43
  | [Medical crawler](https://zenodo.org/record/4561970) | 745,705,946 | Crawler of more than 3,000 URLs belonging to Spanish biomedical and health domains. |
44
  | 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. |
45
  | 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. |
46
- | [Scielo](https://github.com/PlanTL-GOB-ES/SciELO-Spain-Crawler) | 60,007,289 | Publications written in Spanish crawled from the Spanish SciELO server in 2017. |
47
  | [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. |
48
  | 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. |
49
  | 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". |
@@ -118,13 +120,13 @@ If you use our Medical Crawler corpus, please cite the preprint:
118
  ```python
119
  from transformers import AutoTokenizer, AutoModelForMaskedLM
120
 
121
- tokenizer = AutoTokenizer.from_pretrained("PlanTL-GOB-ES/roberta-base-biomedical-es")
122
 
123
- model = AutoModelForMaskedLM.from_pretrained("PlanTL-GOB-ES/roberta-base-biomedical-es")
124
 
125
  from transformers import pipeline
126
 
127
- unmasker = pipeline('fill-mask', model="PlanTL-GOB-ES/roberta-base-biomedical-es")
128
 
129
  unmasker("El único antecedente personal a reseñar era la <mask> arterial.")
130
  ```
@@ -162,7 +164,4 @@ unmasker("El único antecedente personal a reseñar era la <mask> arterial.")
162
  "token_str": " presión"
163
  }
164
  ]
165
- ```
166
-
167
- ## Funding
168
- This work was funded by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) within the framework of the Plan-TL.
 
14
  - text: "En el <mask> toraco-abdómino-pélvico no se encontraron hallazgos patológicos de interés."
15
  ---
16
 
17
+ **⚠️NOTICE⚠️: THIS MODEL HAS BEEN MOVED TO THE FOLLOWING URL AND WILL SOON BE REMOVED:** https://huggingface.co/PlanTL-GOB-ES/roberta-base-biomedical-clinical-es
18
+
19
  # Biomedical-clinical language model for Spanish
20
+ 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._".
21
 
22
  ## Tokenization and model pretraining
23
  This model is a [RoBERTa-based](https://github.com/pytorch/fairseq/tree/master/examples/roberta) model trained on a
 
45
  | [Medical crawler](https://zenodo.org/record/4561970) | 745,705,946 | Crawler of more than 3,000 URLs belonging to Spanish biomedical and health domains. |
46
  | 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. |
47
  | 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. |
48
+ | [Scielo](https://github.com/PlanTL-SANIDAD/SciELO-Spain-Crawler) | 60,007,289 | Publications written in Spanish crawled from the Spanish SciELO server in 2017. |
49
  | [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. |
50
  | 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. |
51
  | 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". |
 
120
  ```python
121
  from transformers import AutoTokenizer, AutoModelForMaskedLM
122
 
123
+ tokenizer = AutoTokenizer.from_pretrained("BSC-TeMU/roberta-base-biomedical-es")
124
 
125
+ model = AutoModelForMaskedLM.from_pretrained("BSC-TeMU/roberta-base-biomedical-es")
126
 
127
  from transformers import pipeline
128
 
129
+ unmasker = pipeline('fill-mask', model="BSC-TeMU/roberta-base-biomedical-es")
130
 
131
  unmasker("El único antecedente personal a reseñar era la <mask> arterial.")
132
  ```
 
164
  "token_str": " presión"
165
  }
166
  ]
167
+ ```
 
 
 
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process.log CHANGED
@@ -3,6 +3,6 @@ Executing train_tokenizer.py
3
  training bbpe tokenizer
4
  Initialize an empty tokenizer
5
  training
6
- saving model tokenizer to /home/shared/dt01/temutauro/ccasimiro/corpus-utils-lm/output/model-ready_output/bio-clinical-vocab-50262-2021-12-07-1604-d1d3-849e/train_tokenizer_output/train-tokenizer-2021-12-07-1625-d1d3-74b8
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3
  training bbpe tokenizer
4
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5
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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/shared/dt01/temutauro/ccasimiro/corpus-utils-lm/output/model-ready_output/bio-clinical-vocab-50262-2021-12-07-1604-d1d3-849e/train_tokenizer_output/train-tokenizer-2021-12-07-1625-d1d3-74b8"}
 
1
+ {"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"}
vocab.json CHANGED
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