Bioformer-LitCovid-v1.4h

sofia-todeschini/Bioformer-LitCovid-v1.4h

Bioformer-LitCovid-v1.4h 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 apache-2.0.

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

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At a glance

Task: Text Classification
Library: transformers
License: apache-2.0
Model type: bert
Access: Public
Created: Jun 25, 2024
Updated: Jun 25, 2024
SHA: bda564d0

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Model card

From the Hugging Face model README

Bioformer-LitCovid-v1.4h

This model is a fine-tuned version of bioformers/bioformer-litcovid on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.5733
Hamming loss: 0.0842
F1 micro: 0.6047
F1 macro: 0.4622
F1 weighted: 0.6887
F1 samples: 0.6127
Precision micro: 0.4576
Precision macro: 0.3466
Precision weighted: 0.5990
Precision samples: 0.5038
Recall micro: 0.8912
Recall macro: 0.8446
Recall weighted: 0.8912
Recall samples: 0.9055
Roc Auc: 0.9044
Accuracy: 0.0708

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: 5.451682398151845e-05
train_batch_size: 32
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.08129918921555689
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
0.9164	1.0	576	0.6810	0.1510	0.4505	0.3468	0.6199	0.4653	0.3057	0.2568	0.5483	0.3450	0.8564	0.8656	0.8564	0.8750	0.8524	0.0078
0.6032	2.0	1152	0.5983	0.1154	0.5273	0.4002	0.6493	0.5373	0.3746	0.2939	0.5587	0.4139	0.8902	0.8651	0.8902	0.9050	0.8872	0.0263
0.4965	3.0	1728	0.5752	0.0975	0.5704	0.4372	0.6709	0.5795	0.4185	0.3237	0.5797	0.4617	0.8952	0.8536	0.8952	0.9089	0.8991	0.0479
0.4354	4.0	2304	0.5655	0.0863	0.5978	0.4554	0.6872	0.6050	0.4508	0.3406	0.6021	0.4948	0.8870	0.8503	0.8870	0.9024	0.9014	0.0636
0.3874	5.0	2880	0.5733	0.0842	0.6047	0.4622	0.6887	0.6127	0.4576	0.3466	0.5990	0.5038	0.8912	0.8446	0.8912	0.9055	0.9044	0.0708

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: apache-2.0
Created: Jun 25, 2024
Updated: Jun 25, 2024