diff --git a/README.md b/README.md index 7b95401dc46245ac339fc25059d4a56d90b4cde5..2584a0737782b686a683d489df6fbe8bc50b8b65 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,205 @@ --- license: apache-2.0 +language: + - en + - es +datasets: + - HiTZ/Multilingual-Medical-Corpus +tags: + - biomedical + - medical + - clinical + - spanish + - multilingual +widget: +- text: "Las radiologías óseas de cuerpo entero no detectan alteraciones , ni alteraciones vertebrales." +- text: "En el toraco-abdómino-pélvico no se encontraron hallazgos patológicos de interés." +- text: "Percutaneous transluminal coronary of both the LAD and Cx was done." +- text: "Clinical examination showed a non-pulsatile, painless, axial, irreducible exophthalmia with no sign of or keratitis, and right monocular blindness, right ptosis." --- + +
+
+ +
+ +

+ EriBERTa +
+ A Bilingual Pre-Trained Language Model +
+ for Clinical Natural Language Processing +

+ +
+ +

+We introduce EriBERTa, a bilingual domain-specific language model pre-trained on extensive medical and clinical corpora. We demonstrate that EriBERTa outperforms previous Spanish language models in the clinical domain, showcasing its superior capabilities in understanding medical texts and extracting meaningful information. +Moreover, EriBERTa exhibits promising transfer learning abilities, allowing for knowledge transfer from one language to another. This aspect is particularly beneficial given the scarcity of Spanish clinical data. +

+ +- 📖 Paper: [EriBERTa: A Bilingual Pre-Trained Language Model for Clinical Natural Language Processing](https://arxiv.org/abs/2306.07373) + + +## How to Get Started with the Model + +You can load the model using: +```python +from transformers import AutoTokenizer, AutoModelForMaskedLM + +tokenizer = AutoTokenizer.from_pretrained("HiTZ/EriBERTa-base") +model = AutoModelForMaskedLM.from_pretrained("HiTZ/EriBERTa-base") +``` + + +# Model Description + +- **Developed by**: Iker De la Iglesia, Aitziber Atutxa, Koldo Gojenola, and Ander Barrena +- **Contact**: [Iker De la Iglesia](mailto:iker.delaiglesia@ehu.eus) and [Ander Barrena](mailto:ander.barrena@ehu.eus) +- **Language(s) (NLP)**: English, Spanish +- **License**: apache-2.0 +- **Funding**: + - The Spanish Ministry of Science and Innovation, MCIN/AEI/ 10.13039/501100011033/FEDER projects: + - Proyectos de Generación de Conocimiento 2022 (EDHIA PID2022-136522OB-C22) + - DOTT-HEALTH/PAT-MED PID2019-543106942RB-C3. + - EU NextGeneration EU/PRTR (DeepR3 TED2021-130295B-C31, ANTIDOTE PCI2020-120717-2 EU ERA-Net CHIST-ERA). + - Basque Government: + - IXA IT1570-22. + + +## Model Details + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Pre-Training settings for EriBERTa-base.
Param. no.~125M
Vocabulary size64k
Sequence Length512
Token/step2M
Steps125k
Total Tokens4.5B
SchedulerLinear with Warm-up
Peak LR2.683e-4
Warm-up Steps7.5k
+ + + + + +## Training Data + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Data sources and word counts by language.
LanguageSourceWords
EnglishClinicalTrials127.4M
EMEA12M
PubMed968.4M
MIMIC-III206M
SpanishEMEA13.6M
PubMed8.4M
Medical Crawler918M
SPACC350K
UFAL10.5M
WikiMed5.2M
+ +# Limitation and Bias + +

EriBERTa is currently optimized for masked language modeling to perform the Fill Mask task. While its potential for fine-tuning on downstream tasks such as Named Entity Recognition (NER) and Text Classification has been evaluated, it is recommended to validate and test the model for specific applications before deploying it in production to ensure its effectiveness and reliability.

+ +

Due to the scarcity of medical-clinical corpora, the EriBERTa model has been trained on a corpus gathered from multiple sources, including web crawling. Thus, the employed corpora may not encompass all possible linguistic and contextual variations present in clinical language. Consequently, the model may exhibit limitations when applied to specific clinical subdomains or rare medical conditions not well-represented in the training data.

+ +## Biases + +
    +
  • Data Collection Bias: The training data for EriBERTa was collected from various sources, some of them using web crawling techniques. This method may introduce biases related to the prevalence of certain types of content, perspectives, and language usage patterns. Consequently, the model might reflect and propagate these biases in its predictions.
    • +
  • Demographic and Linguistic Bias: Given that the web-sourced corpus may not equally represent all demographic groups or linguistic nuances, the model may perform disproportionately well for certain populations while underperforming for others. This could lead to disparities in the quality of clinical data processing and information retrieval across different patient groups.
    • +
  • Unexamined Ethical Considerations: As of now, no comprehensive measures have been taken to systematically evaluate the ethical implications and biases embedded in EriBERTa. While we are committed to addressing these issues, the current version of the model may inadvertently perpetuate existing biases and ethical concerns inherent in the data.
    • +
+ +## Disclaimer +

EriBERTa has not been designed or developed to be used as a medical device. Any output should be verified by a Healthcare Professional, and no direct diagnosis should be claimed. The model's output may not always be completely reliable. Due to the nature of language models, predictions may be incorrect or biased.

+

We do not take any liability for the use of this model, and it should ideally be fine-tuned and tested before application. It must not be used as a medical tool or for any critical decision-making processes without thorough validation and supervision by qualified professionals.

+ + +# Citing information + +```bibtext +@misc{delaiglesia2023eriberta, + title={{EriBERTa: A Bilingual Pre-Trained Language Model for Clinical Natural Language Processing}}, + author={Iker De la Iglesia and Aitziber Atutxa and Koldo Gojenola and Ander Barrena}, + year={2023}, + eprint={2306.07373}, + archivePrefix={arXiv}, + primaryClass={cs.CL} +} +``` diff --git a/config.json b/config.json new file mode 100644 index 0000000000000000000000000000000000000000..a9312eca863313b3931e86e3b1127255d6009dbe --- /dev/null +++ b/config.json @@ -0,0 +1,25 @@ +{ + "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": "4.5.1", + "type_vocab_size": 1, + "use_cache": true, + "vocab_size": 64000 +} diff --git a/eriberta_icon.png b/eriberta_icon.png new file mode 100644 index 0000000000000000000000000000000000000000..33f777dc4cc2ee480fccd58095672d43817d376b Binary files /dev/null and b/eriberta_icon.png differ diff --git a/merges.txt b/merges.txt new file mode 100644 index 0000000000000000000000000000000000000000..26c30e489ee42877a0dfcc84eb540048abba566a --- /dev/null +++ b/merges.txt @@ -0,0 +1,63740 @@ +#version: 0.2 - Trained by `huggingface/tokenizers` +e n +Ġ a +i n +e s +e r +Ġ d +o n +Ġ t +Ġ p +Ġ c +t i +Ġ s +o r +a l +o s +a s +a r +Ġd e +Ġ l +r e +e d +a n +Ġ o +h e +Ġ m +c i +en t +Ġ in +i s +a t +i c +Ġ e +r o +Ġa n +Ġ w +i t +Ġ f +Ġt he +Ġo f +a d +_ _ +a ti +u n +Ġ b +o m +Ġan d +à ³ +q u +Ġl a +Ġ n +i d +e c +Ġ h +Ġ en +Ġ re +o l +Ġc on +in g +u l +u s +e l +Ġ y +Ġ ( +u r +l e +a c +ó n +a m +Ġt o +r es +t er +Ġ qu +t r +Ġ es +i m +a ci +Ġe l +Ġ v +ati on +__ __ +Ġ T +Ġ un +Ġqu e +Ã Ń +i l +Ġ g +Ġt h +Ġ C +t e +it h +Ġs e +Ġw ith +i g +à ¡ +Ġp ro +a b +v e +Ġ A +Ġc om +ti on +t h +u m +u d +c e +er e +Ġ 1 +Ġa l +ti c +Ġ r +i a +Ġs u +Ġl os +i r +Ġf or +i o +t a +t o +Ġ S +Ġ P +m ent +Ġp ar +l y +à © +aci ón +Ġe x +Ġa s +Ġn o +i f +* * +Ġ M +Ġ I +u c +Ġt r +l o +o d +i on +Ġde l +Ġ is +Ġw as +Ġb e +a p +u t +ec t +____ ____ +Ġp er +o g +i z +ati ent +Ġ res +e m +i v +ic al +Ġ 2 +an t +Ġa c +Ġ or +Ġs t +Ġ E +d o +i b +at ed +Ġp atient +a y +Ġl as +p l +Ġd is +o c +ad o +p er +ĠT he +Ġ D +i ci +Ġ R +Ġp or +Ġw ere +ti v +Ġ on +i ent +at e +g e +Ġ im +Ġa d +O N +o p +or m +en ci +Ġes t +in e +Ġm e +Ġ N +Ġ H +u p +v er +es t +Ġp re +ÃŃ a +ro m +Ġpatient s +it y +p e +Ġun a +as e +Ġp r +id ad +Ġpar a +Ġl e +c h +f er +s e +Ġcom p +t or +à ± +Ġ he +el l +o w +y s +Ġc l +u re +Ġc h +c l +i p +or t +Ġin f +ig h +Ġa t +Ġ F +Ġ L +ent e +es s +en s +o ci +en d +Ġb y +Ġ us +Ġd es +0 0 +Ġth at +Ġ B +u e +) . +ul ar +Ġc ar +f f +ol og +Ġ 3 +Ġn e +Ġw h +i al +ig n +ul t +Ġst ud +á s +is t +Ġa p +on es +Ġa re +p h +u al +Ġp res +as t +a in +ad a +Ġh a +er s +d e +Ġ O +Ġcon s +d uc +ĠI n +Ġp os +Ġe v +en ce +ic a +i ti +c re +Ġm ed +u g +e t +à º +Ġo b +a g +Ġin ter +ĠT h +Ġa r +i es +________ ________ +Ġre c +an s +ci ón +ab le +Ġ G +re at +m o +u b +Ġp l +) , +o b +if ic +ar y +os is +al es +Ġim p +ro l +Ġs er +Ġa b +ient o +Ġcon t +m in +an d +v el +i en +en ti +il l +m a +th er +o t +ad os +T I +Ġcom o +Ġf rom +ation s +res s +s t +Ġg r +g r +enci a +Ġ 4 +ent es +Ġs ign +d ic +Ġres p +a v +Ġe ff +Ġ W +e ar +g n +un d +ar i +E S +Ġstud y +Ġ 5 +m on +i or +in ical +an c +Ġd if +Ġ [ +Ġd i +Ġg en +or e +Ġas s +a j +igh t +Ġ 0 +an te +u es +Ġc or +Ġs h +g o +al l +Ġm ás +ti ve +Ġ U +ent r +Ġc ell +Ġno t +I N +d er +i er +á n +i ón +Ġa f +** ] +Ġg ro +Ġl o +Ġc o +u ed +Ġc u +Ġsu b +ar d +on e +Ġcl inical +reat ment +i e +Ġin v +Ġm ay +os e +ic o +ÃŃ n +o us +an do +al iz +at or +Ġth is 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