biolinkbert-large-mnli-resampled

cnut1648/biolinkbert-large-mnli-resampled

biolinkbert-large-mnli-resampled is a text classification model from cnut1648. Use it when you need a label for a piece of text. It is set up for transformers.

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

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At a glance

Task: Text Classification
Library: transformers
Model type: bert
Access: Public
Created: Mar 18, 2023
Updated: Mar 18, 2023
SHA: 4cf72d50

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Datasets

glue

Model card

From the Hugging Face model README

biolinkbert-large-mnli-resampled

This model is a fine-tuned version of BioLinkBERT-large on the resampled version of GLUE MNLI, i.e. cnut1648/mnli_resampled_as_mednli.

The performance is reported below:

Model	Dataset	Acc
Roberta-large-mnli	MNLI dev mm	90.12
	MNLI dev m	90.59
	SNLI test	88.25
BioLinkBERT-large	MNLI dev mm	33.56
	MNLI dev m	33.18
	SNLI test	32.66
BioLinkBERT-large-mnli-snli	MNLI dev mm	85.75
	MNLI dev m	85.30
	SNLI test	89.82
BioLinkBERT-large-mnli-resampled	MNLI dev mm	80.22
	MNLI dev m	78.07
	SNLI test	71.33

Compared to cnut1648/biolinkbert-large-mnli-snli, this checkpoint is never trained on SNLI.

The labels are "0": "entailment", "1": "neutral", "2": "contradiction"

Training procedure

We follow the same training procedure as cnut1648/biolinkbert-large-mnli-snli.

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 32
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 3.0
mixed_precision_training: Native AMP

Framework versions

Transformers 4.25.1
Pytorch 1.13.0+cu117
Datasets 2.4.0
Tokenizers 0.12.1

Task: Text Classification
Library: transformers
Type: bert
Languages: en
Created: Mar 18, 2023
Updated: Mar 18, 2023