camembert_squadFR_question_answering_tools_qlora_fr

AntoineD/camembert_squadFR_question_answering_tools_qlora_fr

camembert_squadFR_question_answering_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.

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

Downloads · 30 days

Access

Public

Updated Nov 10, 2023

Repo size

421 MB

Likes

Public

Hugging Face

Repo makeup

Click a slice to open those files.

.bin5.2 MB · 62%

At a glance

Access: Public
Created: Nov 10, 2023
Updated: Nov 10, 2023
SHA: 67d3e57b

Base models

Model card

From the Hugging Face model README

camembert_squadFR_question_answering_tools_qlora_fr

This model is a fine-tuned version of etalab-ia/camembert-base-squadFR-fquad-piaf on the None dataset. It achieves the following results on the evaluation set:

Loss: 1.3994
Learning Rate: 0.0

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	Rate
No log	1.0	7	3.8843	0.0001
No log	2.0	14	2.5855	0.0001
No log	3.0	21	1.9334	0.0001
No log	4.0	28	1.5336	0.0001
No log	5.0	35	1.2781	0.0001
No log	6.0	42	1.0961	9e-05
No log	7.0	49	1.0501	0.0001
No log	8.0	56	1.0563	0.0001
No log	9.0	63	1.0709	0.0001
No log	10.0	70	1.0806	0.0001
No log	11.0	77	1.1127	0.0001
No log	12.0	84	1.0596	8e-05
No log	13.0	91	1.0592	0.0001
No log	14.0	98	1.0619	0.0001
No log	15.0	105	1.0277	0.0001
No log	16.0	112	0.9977	0.0001
No log	17.0	119	1.0214	0.0001
No log	18.0	126	1.0584	7e-05
No log	19.0	133	1.0866	0.0001
No log	20.0	140	1.1212	0.0001
No log	21.0	147	1.0932	0.0001
No log	22.0	154	1.1510	0.0001
No log	23.0	161	1.1591	0.0001
No log	24.0	168	1.1126	6e-05
No log	25.0	175	1.1220	0.0001
No log	26.0	182	1.1413	0.0001
No log	27.0	189	1.1519	0.0001
No log	28.0	196	1.1718	0.0001
No log	29.0	203	1.2205	0.0001
No log	30.0	210	1.2722	5e-05
No log	31.0	217	1.2453	0.0000
No log	32.0	224	1.2521	0.0000
No log	33.0	231	1.2604	0.0000
No log	34.0	238	1.2694	0.0000
No log	35.0	245	1.2688	0.0000
No log	36.0	252	1.2813	4e-05
No log	37.0	259	1.3230	0.0000
No log	38.0	266	1.3333	0.0000
No log	39.0	273	1.2986	0.0000
No log	40.0	280	1.3076	0.0000
No log	41.0	287	1.3551	0.0000
No log	42.0	294	1.3552	3e-05
No log	43.0	301	1.3435	0.0000
No log	44.0	308	1.3404	0.0000
No log	45.0	315	1.3245	0.0000
No log	46.0	322	1.3288	0.0000
No log	47.0	329	1.3455	0.0000
No log	48.0	336	1.3656	2e-05
No log	49.0	343	1.4061	0.0000
No log	50.0	350	1.4262	0.0000
No log	51.0	357	1.4366	0.0000
No log	52.0	364	1.4267	0.0000
No log	53.0	371	1.4158	0.0000
No log	54.0	378	1.4038	1e-05
No log	55.0	385	1.4013	0.0000
No log	56.0	392	1.4022	0.0000
No log	57.0	399	1.3999	5e-06
No log	58.0	406	1.3987	0.0000
No log	59.0	413	1.3994	0.0000
No log	60.0	420	1.3994	0.0

Framework versions

Transformers 4.34.0
Pytorch 2.0.1+cu117
Datasets 2.14.5
Tokenizers 0.14.1

Created: Nov 10, 2023
Updated: Nov 10, 2023