camembert_classification_tools_qlora_fr

AntoineD/camembert_classification_tools_qlora_fr

camembert_classification_tools_qlora_fr is a machine learning model from AntoineD. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.

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

Downloads · 30 days

Access

Public

Updated Nov 9, 2023

Repo size

478 MB

Likes

Public

Hugging Face

Repo makeup

Click a slice to open those files.

.bin7.6 MB · 70%

At a glance

License: mit
Access: Public
Created: Nov 9, 2023
Updated: Nov 9, 2023
SHA: b7ed6d97

Base models

Model card

From the Hugging Face model README

camembert_classification_tools_qlora_fr

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

Loss: 0.7280
Accuracy: 0.85

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	2.0968	0.075
No log	2.0	10	2.1103	0.075
No log	3.0	15	2.1119	0.075
No log	4.0	20	2.1045	0.1
No log	5.0	25	2.0948	0.125
No log	6.0	30	2.0586	0.125
No log	7.0	35	2.0199	0.2
No log	8.0	40	1.9633	0.25
No log	9.0	45	1.9075	0.35
No log	10.0	50	1.8445	0.5
No log	11.0	55	1.7872	0.55
No log	12.0	60	1.7288	0.6
No log	13.0	65	1.6744	0.625
No log	14.0	70	1.6192	0.65
No log	15.0	75	1.5612	0.65
No log	16.0	80	1.5041	0.65
No log	17.0	85	1.4466	0.7
No log	18.0	90	1.3910	0.675
No log	19.0	95	1.3369	0.7
No log	20.0	100	1.2929	0.725
No log	21.0	105	1.2470	0.725
No log	22.0	110	1.2048	0.725
No log	23.0	115	1.1597	0.725
No log	24.0	120	1.1148	0.775
No log	25.0	125	1.0787	0.775
No log	26.0	130	1.0485	0.775
No log	27.0	135	1.0222	0.75
No log	28.0	140	0.9954	0.8
No log	29.0	145	0.9714	0.85
No log	30.0	150	0.9442	0.825
No log	31.0	155	0.9201	0.85
No log	32.0	160	0.9032	0.85
No log	33.0	165	0.8843	0.85
No log	34.0	170	0.8739	0.85
No log	35.0	175	0.8527	0.85
No log	36.0	180	0.8312	0.85
No log	37.0	185	0.8193	0.85
No log	38.0	190	0.8079	0.85
No log	39.0	195	0.8015	0.85
No log	40.0	200	0.7962	0.85
No log	41.0	205	0.7958	0.85
No log	42.0	210	0.7846	0.85
No log	43.0	215	0.7652	0.85
No log	44.0	220	0.7536	0.85
No log	45.0	225	0.7451	0.85
No log	46.0	230	0.7377	0.85
No log	47.0	235	0.7327	0.85
No log	48.0	240	0.7310	0.85
No log	49.0	245	0.7312	0.85
No log	50.0	250	0.7280	0.85

Framework versions

Transformers 4.34.0
Pytorch 2.0.1+cu117
Datasets 2.14.5
Tokenizers 0.14.1

License: mit
Created: Nov 9, 2023
Updated: Nov 9, 2023