BioCreative-VII-Track-5

Author:

LABRAK Yanis Master 2 – Computer Science

Affiliation:

Laboratoire Informatique d’Avignon (LIA) Natural Language Processing Department

Compatibility issues between Flair 0.8 and 0.9 scripts

Refer to this GitHub issue to solve the compatibility issues or go back to Flair 0.8.

Descriptions

submit_id	label-based micro avg precision	label-based micro avg recall	label-based micro avg f1	label-based macro avg precision	label-based macro avg recall	label-based macro avg f1	instance-based precision	instance-based recall	instance-based f1
BC7_submission_39	0.5130	0.8598	0.6426	0.5240	0.7391	0.5614	0.5965	0.8597	0.7043
BC7_submission_40	0.8760	0.8659	0.8709	0.8498	0.8138	0.8231	0.8981	0.8942	0.8961
BC7_submission_62	0.8699	0.8966	0.8830	0.8298	0.8570	0.8366	0.8993	0.9198	0.9094
BC7_submission_61	0.8951	0.8280	0.8602	0.8814	0.7723	0.8174	0.8787	0.8610	0.8698

BC7 Submission 39

I used class specific keywords extracted from the training dataset (keywords + title + abstract) with a TF-IDF to enhance a HuggingFace PubMedBERT model (microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) adapted to the task by changing the loss function to a BCE one for multi-label classification and running it during 28 epochs with a learning rate of 5e-5.

Folder: model 2 - PubMed Train

BC7 Submission 40

I used a pretrained model called TARS based on the paper "Task-Aware Representation of Sentences for Generic Text Classification" available in the framework Flair to classify documents based only on their abstracts during 50 epochs with a learning rate of 0.02 and with only 85% of the training corpus.

Folder: model 1 - 50 runs flair tars

[Refused] BC7 Submission 42

Folder: model 5 - PubMed Train+Dev

Refused: Due to negative predictions.

BC7 Submission 61

I trained a 1-2-3 gram TF-IDF on both Train and Dev datasets to compute df vectors (dimension 20K) which will represents documents (keywords + title + abstract) in the multi-label SVM classifier.

Folder: model 6 - TF-IDF 1-2-3 gram

BC7 Submission 62

I used class specific keywords extracted from the training dataset (keywords + title + abstract) with a TF-IDF to enhance a pretrained model called TARS based on the paper "Task-Aware Representation of Sentences for Generic Text Classification" available in the framework Flair adapted to the task for multi-label classification and running it during 10 epochs on Train only with a learning rate of 0.02.

Folder: model 4 - flair all + ner

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