metadata

license: mit
base_model: roberta-large
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - recall
  - f1
model-index:
  - name: lora-roberta-large-no-anger-f4-0927
    results: []
library_name: peft

lora-roberta-large-no-anger-f4-0927

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

Loss: 0.7106
Accuracy: 0.7405
Prec: 0.7387
Recall: 0.7405
F1: 0.7387
B Acc: 0.5982
Micro F1: 0.7405
Prec Joy: 0.7558
Recall Joy: 0.7617
F1 Joy: 0.7587
Prec Anger: 0.6294
Recall Anger: 0.5631
F1 Anger: 0.5944
Prec Disgust: 0.4637
Recall Disgust: 0.3854
F1 Disgust: 0.4209
Prec Fear: 0.4892
Recall Fear: 0.5817
F1 Fear: 0.5315
Prec Neutral: 0.8292
Recall Neutral: 0.8481
F1 Neutral: 0.8385
Prec Sadness: 0.6600
Recall Sadness: 0.6140
F1 Sadness: 0.6362
Prec Surprise: 0.5320
Recall Surprise: 0.4331
F1 Surprise: 0.4775

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: 0.001
train_batch_size: 32
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 128
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.05
num_epochs: 25.0

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Prec	Recall	F1	B Acc	Micro F1	Prec Joy	Recall Joy	F1 Joy	Prec Anger	Recall Anger	F1 Anger	Prec Disgust	Recall Disgust	F1 Disgust	Prec Fear	Recall Fear	F1 Fear	Prec Neutral	Recall Neutral	F1 Neutral	Prec Sadness	Recall Sadness	F1 Sadness	Prec Surprise	Recall Surprise	F1 Surprise
0.8167	1.25	2049	0.7756	0.7130	0.7003	0.7130	0.7030	0.5272	0.7130	0.7252	0.7430	0.7340	0.6026	0.3749	0.4622	0.4187	0.3646	0.3898	0.5369	0.4170	0.4694	0.7763	0.8629	0.8173	0.6123	0.5784	0.5949	0.4797	0.3495	0.4044
0.7639	2.5	4098	0.7302	0.7293	0.7206	0.7293	0.7224	0.5662	0.7293	0.7361	0.7617	0.7487	0.6187	0.5198	0.5649	0.3881	0.4229	0.4048	0.5851	0.4247	0.4922	0.7961	0.8570	0.8254	0.6380	0.6185	0.6281	0.532	0.3585	0.4283
0.7395	3.75	6147	0.7348	0.7287	0.7328	0.7287	0.7271	0.5793	0.7287	0.6989	0.8136	0.7519	0.6786	0.4384	0.5327	0.4180	0.3875	0.4022	0.4632	0.5830	0.5162	0.8480	0.8134	0.8303	0.6648	0.5950	0.6280	0.5210	0.4241	0.4676
0.789	5.0	8196	0.7419	0.7275	0.7206	0.7275	0.7180	0.5511	0.7275	0.6888	0.8113	0.7450	0.6014	0.5183	0.5568	0.4038	0.4021	0.4029	0.5747	0.4305	0.4923	0.8063	0.8420	0.8238	0.6861	0.5838	0.6308	0.6224	0.2695	0.3762
0.7439	6.25	10245	0.7608	0.7207	0.7317	0.7207	0.7224	0.5858	0.7207	0.6882	0.8143	0.7459	0.6198	0.5004	0.5537	0.3944	0.3542	0.3732	0.4556	0.5843	0.5120	0.8599	0.7888	0.8228	0.7047	0.5590	0.6235	0.4535	0.4996	0.4754
0.712	7.5	12294	0.7240	0.7298	0.7270	0.7298	0.7263	0.5809	0.7298	0.7057	0.8043	0.7518	0.6313	0.4795	0.5450	0.4141	0.4271	0.4205	0.5707	0.4517	0.5043	0.8329	0.8214	0.8271	0.6126	0.6459	0.6288	0.5209	0.4367	0.4751
0.7032	8.75	14343	0.7095	0.7344	0.7328	0.7344	0.7317	0.5833	0.7344	0.7557	0.7479	0.7518	0.6391	0.5302	0.5796	0.4311	0.3521	0.3876	0.4724	0.6062	0.5310	0.8188	0.8498	0.8340	0.6472	0.6140	0.6301	0.5605	0.3827	0.4549
0.6972	10.0	16392	0.7108	0.7343	0.7325	0.7343	0.7317	0.5923	0.7343	0.7158	0.8038	0.7572	0.5785	0.5474	0.5625	0.3615	0.4729	0.4097	0.5714	0.4865	0.5255	0.8322	0.8288	0.8305	0.6797	0.5973	0.6358	0.5403	0.4097	0.4660
0.6859	11.25	18441	0.7211	0.7376	0.7321	0.7376	0.7322	0.5792	0.7376	0.7067	0.8093	0.7545	0.6216	0.5325	0.5736	0.4119	0.4188	0.4153	0.5720	0.4755	0.5193	0.8264	0.8407	0.8335	0.6677	0.6099	0.6375	0.5876	0.3675	0.4522
0.6542	12.5	20490	0.7143	0.7347	0.7294	0.7347	0.7307	0.5817	0.7347	0.7358	0.7824	0.7584	0.6263	0.5407	0.5804	0.3931	0.3792	0.3860	0.5700	0.4665	0.5131	0.8203	0.8364	0.8283	0.6158	0.6658	0.6398	0.5400	0.4007	0.4600
0.6463	13.75	22539	0.7022	0.7369	0.7366	0.7369	0.7354	0.5947	0.7369	0.7371	0.7864	0.7610	0.5452	0.6393	0.5885	0.5170	0.3167	0.3928	0.5519	0.4858	0.5168	0.8455	0.8218	0.8335	0.6062	0.6649	0.6342	0.5320	0.4483	0.4866
0.6333	15.0	24588	0.7106	0.7405	0.7387	0.7405	0.7387	0.5982	0.7405	0.7558	0.7617	0.7587	0.6294	0.5631	0.5944	0.4637	0.3854	0.4209	0.4892	0.5817	0.5315	0.8292	0.8481	0.8385	0.6600	0.6140	0.6362	0.5320	0.4331	0.4775
0.6184	16.25	26637	0.7199	0.7338	0.7389	0.7338	0.7348	0.6077	0.7338	0.7207	0.8008	0.7586	0.6140	0.5571	0.5842	0.3692	0.4292	0.3969	0.5024	0.5972	0.5457	0.8534	0.8079	0.8301	0.6714	0.6	0.6337	0.5109	0.4618	0.4851
0.5916	17.5	28686	0.7220	0.7368	0.7376	0.7368	0.7363	0.6003	0.7368	0.7426	0.7859	0.7636	0.5858	0.5713	0.5784	0.3743	0.4125	0.3925	0.5766	0.4653	0.5150	0.8479	0.8258	0.8367	0.5879	0.6676	0.6252	0.5146	0.4735	0.4932
0.5823	18.75	30735	0.7228	0.7376	0.7374	0.7376	0.7364	0.5960	0.7376	0.7210	0.8058	0.7610	0.6206	0.5534	0.5851	0.4056	0.3625	0.3828	0.5199	0.5631	0.5406	0.8460	0.8200	0.8328	0.6599	0.6126	0.6354	0.5254	0.4546	0.4875
0.5728	20.0	32784	0.7313	0.7344	0.7365	0.7344	0.7349	0.6090	0.7344	0.7295	0.7934	0.7601	0.5795	0.5907	0.5851	0.3927	0.4271	0.4092	0.5434	0.5161	0.5294	0.8462	0.8115	0.8285	0.6541	0.6311	0.6424	0.4928	0.4933	0.4930
0.5562	21.25	34833	0.7414	0.7376	0.7372	0.7376	0.7366	0.5995	0.7376	0.7372	0.7934	0.7643	0.6308	0.5258	0.5735	0.3946	0.425	0.4092	0.5324	0.5341	0.5332	0.8433	0.8267	0.8349	0.6139	0.6374	0.6254	0.5249	0.4537	0.4867
0.5348	22.5	36882	0.7398	0.7370	0.7374	0.7370	0.7365	0.6017	0.7370	0.7268	0.8039	0.7634	0.5844	0.5892	0.5868	0.4013	0.3937	0.3975	0.5331	0.5238	0.5284	0.8488	0.8163	0.8322	0.6473	0.6275	0.6372	0.5194	0.4573	0.4864
0.5202	23.75	38931	0.7423	0.7389	0.7379	0.7389	0.7381	0.6013	0.7389	0.7415	0.7893	0.7646	0.6020	0.5728	0.5871	0.4013	0.3896	0.3953	0.5341	0.5296	0.5318	0.8416	0.8279	0.8347	0.6410	0.6338	0.6374	0.5093	0.4663	0.4869

Framework versions

Transformers 4.33.1
Pytorch 2.0.1
Datasets 2.12.0
Tokenizers 0.13.3