Qt10Classic_MSE_Sample

YakovElm/Qt10Classic_MSE_Sample

Qt10Classic_MSE_Sample is a text classification model from YakovElm. 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.

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 10, 2023
Updated: Jun 10, 2023
SHA: ab172699

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Model card

From the Hugging Face model README

Qt10Classic_MSE_Sample

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

Train Loss: 0.0054
Train Accuracy: 0.4946
Validation Loss: 0.0045
Validation Accuracy: 0.4031
Epoch: 2

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:

optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
training_precision: float32

Training results

Train Loss	Train Accuracy	Validation Loss	Validation Accuracy	Epoch
0.0063	0.4951	0.0043	0.0641	0
0.0057	0.5049	0.0042	0.7453	1
0.0054	0.4946	0.0045	0.4031	2

Framework versions

Transformers 4.29.2
TensorFlow 2.12.0
Datasets 2.12.0
Tokenizers 0.13.3

Task: Text Classification
Library: transformers
Type: bert
License: apache-2.0
Created: Jun 10, 2023
Updated: Jun 10, 2023