hBERTv1_new_pretrain_48_emb_com_qnli

gokuls/hBERTv1_new_pretrain_48_emb_com_qnli

hBERTv1_new_pretrain_48_emb_com_qnli is a text classification model from gokuls. 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: hybridbert
Access: Public
Created: Jun 14, 2023
Updated: Jun 14, 2023
SHA: 3b4edb8e

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Datasets

glue

Model card

From the Hugging Face model README

hBERTv1_new_pretrain_48_emb_com_qnli

This model is a fine-tuned version of gokuls/bert_12_layer_model_v1_complete_training_new_emb_compress_48 on the GLUE QNLI dataset. It achieves the following results on the evaluation set:

Loss: 0.6611
Accuracy: 0.5984

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: 4e-05
train_batch_size: 128
eval_batch_size: 128
seed: 10
distributed_type: multi-GPU
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.6862	1.0	819	0.6995	0.5513
0.6677	2.0	1638	0.6624	0.5949
0.6553	3.0	2457	0.6611	0.5984
0.6426	4.0	3276	0.6866	0.5762
0.6244	5.0	4095	0.6751	0.5951
0.6136	6.0	4914	0.6903	0.5728
0.6036	7.0	5733	0.7188	0.5731
0.5855	8.0	6552	0.7175	0.5825

Framework versions

Transformers 4.30.2
Pytorch 1.14.0a0+410ce96
Datasets 2.12.0
Tokenizers 0.13.3

Task: Text Classification
Library: transformers
Type: hybridbert
Languages: en
Created: Jun 14, 2023
Updated: Jun 14, 2023