distilbert-base-uncased-finetuned-m_express_emo

Gregorig/distilbert-base-uncased-finetuned-m_express_emo

distilbert-base-uncased-finetuned-m_express_emo is a text classification model from Gregorig. 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. --

Downloads · 30 days

21% of all-time downloads

All-time downloads

Public

Parameters

67M

268 MB on disk

Likes

Public

Hugging Face

Repo makeup

Click a slice to open those files.

.safetensors268 MB · 100%

At a glance

Task: Text Classification
Library: transformers
License: apache-2.0
Model type: distilbert
Access: Public
Created: Jun 5, 2024
Updated: Jun 5, 2024
SHA: 0b313aaf

Try a prompt

Base models

Model card

From the Hugging Face model README

distilbert-base-uncased-finetuned-m_express_emo

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

Loss: 0.4812
Accuracy: 0.815
F1: 0.8166

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: 2e-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
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
0.6532	1.0	26	0.5993	0.705	0.7217
0.5897	2.0	52	0.5433	0.75	0.7625
0.531	3.0	78	0.5017	0.785	0.7909
0.4727	4.0	104	0.4837	0.8	0.8042
0.4485	5.0	130	0.4812	0.815	0.8166

Framework versions

Transformers 4.41.1
Pytorch 2.3.0+cu121
Tokenizers 0.19.1

Task: Text Classification
Library: transformers
Type: distilbert
License: apache-2.0
Created: Jun 5, 2024
Updated: Jun 5, 2024