256model_v2

umangsharmacs/256model_v2

256model_v2 is a text classification model from umangsharmacs. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as mit.

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

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At a glance

Task: Text Classification
Library: transformers
License: mit
Model type: roberta
Access: Public
Created: Oct 23, 2024
Updated: Oct 23, 2024
SHA: 17cd039e

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Base models

Model card

From the Hugging Face model README

256model_v2

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

Loss: 0.5044
Accuracy: 0.8050
F1: 0.7986
Precision: 0.7973
Recall: 0.8050

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: 5e-05
train_batch_size: 64
eval_batch_size: 64
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
0.5612	1.0	100	0.5241	0.7170	0.5988	0.5141	0.7170
0.5115	2.0	200	0.4890	0.7761	0.7302	0.7900	0.7761
0.4162	3.0	300	0.4183	0.8151	0.8055	0.8076	0.8151
0.4035	4.0	400	0.4102	0.8063	0.7996	0.7985	0.8063
0.31	5.0	500	0.5044	0.8050	0.7986	0.7973	0.8050

Framework versions

Transformers 4.45.1
Pytorch 2.4.0
Datasets 3.0.1
Tokenizers 0.20.0

Task: Text Classification
Library: transformers
Type: roberta
License: mit
Created: Oct 23, 2024
Updated: Oct 23, 2024