camembert_classification_tools_qlora

AntoineD/camembert_classification_tools_qlora

camembert_classification_tools_qlora is a text classification model from AntoineD. 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: camembert
Access: Public
Created: Nov 7, 2023
Updated: Nov 8, 2023
SHA: 71209b6f

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Base models

Model card

From the Hugging Face model README

camembert_classification_tools_qlora

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

Loss: 0.7844
Accuracy: 0.7

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: 24
eval_batch_size: 192
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 60

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	5	2.0913	0.05
No log	2.0	10	2.1000	0.05
No log	3.0	15	2.0985	0.1
No log	4.0	20	2.0854	0.15
No log	5.0	25	2.0667	0.275
No log	6.0	30	2.0459	0.35
No log	7.0	35	2.0092	0.325
No log	8.0	40	1.9610	0.375
No log	9.0	45	1.9182	0.4
No log	10.0	50	1.8769	0.425
No log	11.0	55	1.8349	0.425
No log	12.0	60	1.7894	0.425
No log	13.0	65	1.7395	0.425
No log	14.0	70	1.6914	0.425
No log	15.0	75	1.6472	0.45
No log	16.0	80	1.6029	0.45
No log	17.0	85	1.5619	0.475
No log	18.0	90	1.5190	0.5
No log	19.0	95	1.4621	0.575
No log	20.0	100	1.4180	0.55
No log	21.0	105	1.3786	0.575
No log	22.0	110	1.3384	0.575
No log	23.0	115	1.2975	0.625
No log	24.0	120	1.2561	0.65
No log	25.0	125	1.2164	0.675
No log	26.0	130	1.1839	0.675
No log	27.0	135	1.1602	0.65
No log	28.0	140	1.1304	0.625
No log	29.0	145	1.1029	0.625
No log	30.0	150	1.0744	0.625
No log	31.0	155	1.0482	0.625
No log	32.0	160	1.0197	0.675
No log	33.0	165	0.9967	0.725
No log	34.0	170	0.9793	0.725
No log	35.0	175	0.9640	0.725
No log	36.0	180	0.9502	0.675
No log	37.0	185	0.9390	0.65
No log	38.0	190	0.9183	0.7
No log	39.0	195	0.8987	0.725
No log	40.0	200	0.8817	0.775
No log	41.0	205	0.8684	0.725
No log	42.0	210	0.8611	0.7
No log	43.0	215	0.8607	0.7
No log	44.0	220	0.8592	0.7
No log	45.0	225	0.8471	0.725
No log	46.0	230	0.8306	0.725
No log	47.0	235	0.8189	0.75
No log	48.0	240	0.8136	0.725
No log	49.0	245	0.8142	0.7
No log	50.0	250	0.8092	0.7
No log	51.0	255	0.8053	0.75
No log	52.0	260	0.7995	0.75
No log	53.0	265	0.7917	0.75
No log	54.0	270	0.7901	0.725
No log	55.0	275	0.7910	0.7
No log	56.0	280	0.7904	0.7
No log	57.0	285	0.7884	0.7
No log	58.0	290	0.7863	0.7
No log	59.0	295	0.7851	0.7
No log	60.0	300	0.7844	0.7

Framework versions

Transformers 4.34.0
Pytorch 2.0.1+cu117
Datasets 2.14.5
Tokenizers 0.14.1

Task: Text Classification
Library: transformers
Type: camembert
License: mit
Created: Nov 7, 2023
Updated: Nov 8, 2023