PubMedBERT-Large-LitCovid-v1.3hh

sofia-todeschini/PubMedBERT-Large-LitCovid-v1.3hh

PubMedBERT-Large-LitCovid-v1.3hh 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: Mar 9, 2024
Updated: Mar 10, 2024
SHA: 81eac500

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Model card

From the Hugging Face model README

PubMedBERT-Large-LitCovid-v1.3hh

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

Loss: 1.0491
Hamming loss: 0.0120
F1 micro: 0.8926
F1 macro: 0.5147
F1 weighted: 0.9005
F1 samples: 0.8991
Precision micro: 0.8601
Precision macro: 0.4443
Precision weighted: 0.8773
Precision samples: 0.8949
Recall micro: 0.9276
Recall macro: 0.6794
Recall weighted: 0.9276
Recall samples: 0.9391
Roc Auc: 0.9595
Accuracy: 0.7345

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: 5e-05
train_batch_size: 4
eval_batch_size: 4
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 8
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.11455156472885997
num_epochs: 5
mixed_precision_training: Native AMP

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
1.7664	1.0	4543	0.7307	0.0260	0.7934	0.3058	0.8407	0.8375	0.6920	0.2476	0.7786	0.8050	0.9296	0.6254	0.9296	0.9405	0.9531	0.5695
1.2652	2.0	9086	0.6934	0.0179	0.8492	0.4112	0.8710	0.8656	0.7749	0.3399	0.8188	0.8377	0.9394	0.6981	0.9394	0.9481	0.9620	0.6367
1.0967	3.0	13629	0.6460	0.0159	0.8631	0.4208	0.8849	0.8761	0.8008	0.3493	0.8460	0.8555	0.9360	0.7053	0.9360	0.9446	0.9614	0.6683
0.7557	4.0	18172	0.8881	0.0123	0.8901	0.5048	0.8966	0.8952	0.8527	0.4416	0.8674	0.8865	0.9309	0.6630	0.9309	0.9409	0.9609	0.7224
0.4182	5.0	22715	1.0491	0.0120	0.8926	0.5147	0.9005	0.8991	0.8601	0.4443	0.8773	0.8949	0.9276	0.6794	0.9276	0.9391	0.9595	0.7345

Framework versions

Transformers 4.28.0
Pytorch 2.1.0+cu121
Datasets 2.18.0
Tokenizers 0.13.3

Task: Text Classification
Library: transformers
Type: bert
License: mit
Created: Mar 9, 2024
Updated: Mar 10, 2024