PubMedELECTRA-LitCovid-v1.3.1

sofia-todeschini/PubMedELECTRA-LitCovid-v1.3.1

PubMedELECTRA-LitCovid-v1.3.1 is a text classification model from sofia-todeschini. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as mit.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Text Classification
Library: transformers
License: mit
Model type: bert
Access: Public
Created: Nov 2, 2023
Updated: Nov 2, 2023
SHA: 04760b26

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Model card

From the Hugging Face model README

PubMedELECTRA-LitCovid-v1.3.1

This model is a fine-tuned version of microsoft/BiomedNLP-PubMedELECTRA-base-uncased-abstract on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.6642
Hamming loss: 0.0230
F1 micro: 0.8128
F1 macro: 0.3182
F1 weighted: 0.8716
F1 samples: 0.8709
Precision micro: 0.7205
Precision macro: 0.2593
Precision weighted: 0.8282
Precision samples: 0.8563
Recall micro: 0.9323
Recall macro: 0.7360
Recall weighted: 0.9323
Recall samples: 0.9428
Roc Auc: 0.9559
Accuracy: 0.6786

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Hamming loss	F1 micro	F1 macro	F1 weighted	F1 samples	Precision micro	Precision macro	Precision weighted	Precision samples	Recall micro	Recall macro	Recall weighted	Recall samples	Roc Auc	Accuracy
1.2083	1.0	2272	0.5767	0.0441	0.6908	0.2460	0.8045	0.8120	0.5536	0.1953	0.7313	0.7793	0.9184	0.7253	0.9184	0.9322	0.9382	0.5340
1.1155	2.0	4544	0.5897	0.0282	0.7778	0.2895	0.8483	0.8513	0.6729	0.2337	0.7989	0.8313	0.9214	0.6944	0.9214	0.9355	0.9480	0.6247
0.9687	3.0	6816	0.5859	0.0261	0.7937	0.3031	0.8573	0.8605	0.6880	0.2446	0.8017	0.8359	0.9378	0.7219	0.9378	0.9481	0.9569	0.6418
0.7527	4.0	9088	0.6337	0.0232	0.8115	0.3152	0.8694	0.8707	0.7184	0.2571	0.8240	0.8553	0.9323	0.7123	0.9323	0.9429	0.9558	0.6752
0.4783	5.0	11360	0.6642	0.0230	0.8128	0.3182	0.8716	0.8709	0.7205	0.2593	0.8282	0.8563	0.9323	0.7360	0.9323	0.9428	0.9559	0.6786

Framework versions

Transformers 4.28.0
Pytorch 2.1.0+cu118
Datasets 2.14.6
Tokenizers 0.13.3

Task: Text Classification
Library: transformers
Type: bert
License: mit
Created: Nov 2, 2023
Updated: Nov 2, 2023