minirbt-h256-fin-finetuned

pawaca/minirbt-h256-fin-finetuned

minirbt-h256-fin-finetuned is a text classification model from pawaca. 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: Feb 17, 2023
Updated: Apr 20, 2023
SHA: 9fc32751

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Model card

From the Hugging Face model README

minirbt-h256-fin-finetuned

This model is a fine-tuned version of hfl/minirbt-h256 on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.4252
Accuracy: 0.8808
F1: 0.8813

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: 0.0001
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 4

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
0.4226	1.0	1096	0.4544	0.8569	0.8576
0.3963	2.0	2192	0.3878	0.8800	0.8806
0.324	3.0	3288	0.4218	0.8779	0.8784
0.3088	4.0	4384	0.4252	0.8808	0.8813

Framework versions

Transformers 4.26.1
Pytorch 2.0.0.dev20230212
Datasets 2.9.0
Tokenizers 0.13.2

Task: Text Classification
Library: transformers
Type: bert
License: apache-2.0
Created: Feb 17, 2023
Updated: Apr 20, 2023