PubMedBERT-Large-LitCovid-1.4

sofia-todeschini/PubMedBERT-Large-LitCovid-1.4

PubMedBERT-Large-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. --

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At a glance

Task: Text Classification
Library: transformers
License: mit
Model type: bert
Access: Public
Created: Jun 25, 2024
Updated: Jun 25, 2024
SHA: 68a5ef6d

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Model card

From the Hugging Face model README

PubMedBERT-Large-LitCovid-1.4

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: 0.6105
Hamming loss: 0.0623
F1 micro: 0.6724
F1 macro: 0.5303
F1 weighted: 0.7292
F1 samples: 0.6741
Precision micro: 0.5423
Precision macro: 0.4146
Precision weighted: 0.6499
Precision samples: 0.5845
Recall micro: 0.8849
Recall macro: 0.8178
Recall weighted: 0.8849
Recall samples: 0.9022
Roc Auc: 0.9133
Accuracy: 0.1313

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: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
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
0.589	1.0	1151	0.5719	0.1031	0.5554	0.4307	0.6704	0.5629	0.4034	0.3213	0.5843	0.4435	0.8909	0.8673	0.8909	0.9062	0.8941	0.0363
0.4668	2.0	2302	0.5438	0.0836	0.6082	0.4623	0.6974	0.6147	0.4599	0.3478	0.6098	0.5052	0.8976	0.8556	0.8976	0.9123	0.9077	0.0774
0.3791	3.0	3453	0.5510	0.0790	0.6225	0.4829	0.7070	0.6247	0.4754	0.3661	0.6205	0.5140	0.9012	0.8541	0.9012	0.9165	0.9119	0.0759
0.307	4.0	4605	0.5954	0.0635	0.6688	0.5235	0.7280	0.6689	0.5371	0.4078	0.6477	0.5767	0.8863	0.8212	0.8863	0.9036	0.9134	0.1229
0.2687	5.0	5755	0.6105	0.0623	0.6724	0.5303	0.7292	0.6741	0.5423	0.4146	0.6499	0.5845	0.8849	0.8178	0.8849	0.9022	0.9133	0.1313

Framework versions

Transformers 4.28.0
Pytorch 2.3.0+cu121
Datasets 2.20.0
Tokenizers 0.13.3

Task: Text Classification
Library: transformers
Type: bert
License: mit
Created: Jun 25, 2024
Updated: Jun 25, 2024