Q9-PHQ

ishwarbb23/Q9-PHQ

Q9-PHQ is a text classification model from ishwarbb23. 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

9% of all-time downloads

All-time downloads

Public

Parameters

67M

1.3 GB 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: Nov 3, 2023
Updated: Nov 3, 2023
SHA: 2e951796

Try a prompt

Base models

Model card

From the Hugging Face model README

Q9-PHQ

This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.3267
Accuracy: 0.8875
Mcc: 0.6295

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: 16
eval_batch_size: 16
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	Mcc
No log	1.0	51	0.4744	0.8075	0.0
No log	2.0	102	0.3437	0.8825	0.5807
No log	3.0	153	0.3191	0.8825	0.6130
No log	4.0	204	0.3412	0.8725	0.6176
No log	5.0	255	0.3267	0.8875	0.6295

Framework versions

Transformers 4.35.0
Pytorch 2.1.0+cu118
Datasets 2.14.6
Tokenizers 0.14.1

Task: Text Classification
Library: transformers
Type: distilbert
License: apache-2.0
Created: Nov 3, 2023
Updated: Nov 3, 2023