Rollback to the first model version

.gitattributes

@@ -25,3 +25,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zstandard filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+pytorch_model.bin filter=lfs diff=lfs merge=lfs -text

README.md

@@ -14,8 +14,10 @@ widget:
 - text: "En el <mask> toraco-abdómino-pélvico no se encontraron hallazgos patológicos de interés."
 ---
 
+**⚠️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
+
 # 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-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._".
+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
@@ -43,7 +45,7 @@ Eventually, the clinical corpus is concatenated to the cleaned biomedical corpus
 | [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-GOB-ES/SciELO-Spain-Crawler)                        | 60,007,289  | Publications written in Spanish crawled from the Spanish SciELO server in 2017.                                                                                                                                       |
+| [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".                                                        |
@@ -118,13 +120,13 @@ If you use our Medical Crawler corpus, please cite the preprint:
 ```python
 from transformers import AutoTokenizer, AutoModelForMaskedLM
 
-tokenizer = AutoTokenizer.from_pretrained("PlanTL-GOB-ES/roberta-base-biomedical-es")
+tokenizer = AutoTokenizer.from_pretrained("BSC-TeMU/roberta-base-biomedical-es")
 
-model = AutoModelForMaskedLM.from_pretrained("PlanTL-GOB-ES/roberta-base-biomedical-es")
+model = AutoModelForMaskedLM.from_pretrained("BSC-TeMU/roberta-base-biomedical-es")
 
 from transformers import pipeline
 
-unmasker = pipeline('fill-mask', model="PlanTL-GOB-ES/roberta-base-biomedical-es")
+unmasker = pipeline('fill-mask', model="BSC-TeMU/roberta-base-biomedical-es")
 
 unmasker("El único antecedente personal a reseñar era la <mask> arterial.")
 ```
@@ -162,7 +164,4 @@ unmasker("El único antecedente personal a reseñar era la <mask> arterial.")
     "token_str": " presión"
   }
 ]
-```
-
-## Funding
-This work was funded by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) within the framework of the Plan-TL.
+```

args.json

@@ -1,13 +1,14 @@
 {
-    "tokenizer_path_name": null,
-    "vocab_name": "bio-clinical",
-    "tokenizer": "bbpe-roberta",
+    "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": 50262,
-    "min_frequency": 6,
+    "vocab_size": 52000,
+    "min_frequency": 10,
     "extra_tokens": [],
     "limit_alphabet": 1000,
-    "max_len": 512,
     "no_show_progress": false,
     "strip_accents": false,
     "no_handle_chinese_chars": false,
@@ -15,14 +16,9 @@
     "reserve_tokens": 0,
     "use_tokenizers": false,
     "no_fairseq": false,
-    "bbpe_add_prefix_space": true,
-    "single_paragraph_add_punct": true,
-    "tok_batch_size": 100000000,
     "files": [
-        "/home/shared/dt01/temutauro/ccasimiro/corpus-utils-lm/output/model-ready_output/bio-clinical-vocab-50262-2021-12-07-1604-d1d3-849e/train_valid_test_split_output/bio-clinical-2021-12-07-1608-d1d3-fb2f/train.txt",
-        "/home/shared/dt01/temutauro/ccasimiro/corpus-utils-lm/output/model-ready_output/bio-clinical-vocab-50262-2021-12-07-1604-d1d3-849e/train_valid_test_split_output/bio-clinical-2021-12-07-1608-d1d3-fb2f/valid.txt",
-        "/home/shared/dt01/temutauro/ccasimiro/corpus-utils-lm/output/model-ready_output/bio-clinical-vocab-50262-2021-12-07-1604-d1d3-849e/train_valid_test_split_output/bio-clinical-2021-12-07-1608-d1d3-fb2f/test.txt"
+        "/home/usuaris/veu/casimiro.pio.carrino/projects/corpus-utils-lm/corpora/bio/biomedical-clinical.txt"
     ],
-    "output_root_path": "/home/shared/dt01/temutauro/ccasimiro/corpus-utils-lm/output/model-ready_output/bio-clinical-vocab-50262-2021-12-07-1604-d1d3-849e",
-    "commit_hash": "d1d3920e7012caf14c9d6968fded36e0dd719a51"
+    "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

@@ -18,8 +18,8 @@
   "num_hidden_layers": 12,
   "pad_token_id": 1,
   "position_embedding_type": "absolute",
-  "transformers_version": "4.6.0",
+  "transformers_version": "4.4.0",
   "type_vocab_size": 1,
   "use_cache": true,
-  "vocab_size": 50262
+  "vocab_size": 52000
 }

dict.txt

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process.log

@@ -3,6 +3,6 @@ Executing train_tokenizer.py
 training bbpe tokenizer
 Initialize an empty tokenizer
 training
-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
-saving pretrained 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
-saving config 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
+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

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:8465af10873f1fff9361fd90d493db06f130f7065c0c9122951f0225b860b42c
-size 499067539
+oid sha256:2e0c8d7bb348e40327b7c38eb6995c74d5c64345bc4ab9b3deff58e7359f15f7
+size 504420627

tokenizer_config.json

@@ -1 +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/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"}
+{"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"}