PubMedBERT-LitCovid-1.4

sofia-todeschini/PubMedBERT-LitCovid-1.4

PubMedBERT-LitCovid-1.4 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. --

Downloads · 30 days

29% of all-time downloads

All-time downloads

Public

Repo size

2.6 GB

Likes

Public

Hugging Face

Repo makeup

Click a slice to open those files.

.bin438 MB · 100%

At a glance

Task: Text Classification
Library: transformers
License: mit
Model type: bert
Access: Public
Created: Jun 26, 2024
Updated: Jun 26, 2024
SHA: bb2e55aa

Try a prompt

Model card

From the Hugging Face model README

PubMedBERT-LitCovid-1.4

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

Loss: 0.5628
Hamming loss: 0.0745
F1 micro: 0.6343
F1 macro: 0.4913
F1 weighted: 0.7105
F1 samples: 0.6391
Precision micro: 0.4918
Precision macro: 0.3747
Precision weighted: 0.6260
Precision samples: 0.5363
Recall micro: 0.8930
Recall macro: 0.8406
Recall weighted: 0.8930
Recall samples: 0.9098
Roc Auc: 0.9106
Accuracy: 0.0952

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
gradient_accumulation_steps: 2
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
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
0.6486	1.0	1151	0.6207	0.1099	0.5362	0.4107	0.6522	0.5433	0.3858	0.3021	0.5651	0.4237	0.8791	0.8500	0.8791	0.8964	0.8850	0.0234
0.5189	2.0	2303	0.5572	0.0981	0.5696	0.4299	0.6739	0.5815	0.4170	0.3178	0.5825	0.4655	0.8984	0.8672	0.8984	0.9143	0.9002	0.0501
0.4426	3.0	3454	0.5516	0.0853	0.6029	0.4632	0.6947	0.6086	0.4545	0.3493	0.6085	0.4966	0.8951	0.8538	0.8951	0.9116	0.9057	0.0650
0.3771	4.0	4606	0.5647	0.0735	0.6371	0.4944	0.7110	0.6402	0.4955	0.3779	0.6258	0.5377	0.8920	0.8363	0.8920	0.9087	0.9106	0.0924
0.3467	5.0	5755	0.5628	0.0745	0.6343	0.4913	0.7105	0.6391	0.4918	0.3747	0.6260	0.5363	0.8930	0.8406	0.8930	0.9098	0.9106	0.0952

Framework versions

Transformers 4.28.0
Pytorch 2.0.0
Datasets 2.1.0
Tokenizers 0.13.3

Task: Text Classification
Library: transformers
Type: bert
License: mit
Created: Jun 26, 2024
Updated: Jun 26, 2024