twitter-xlmr-clip-finetuned-all-123/README.md

metadata

base_model: cardiffnlp/twitter-xlm-roberta-base-sentiment-multilingual
tags:
  - generated_from_trainer
datasets:
  - all
metrics:
  - precision
  - recall
  - f1
model-index:
  - name: twitter-xlmr-clip-finetuned-all-123
    results: []

twitter-xlmr-clip-finetuned-all-123

This model is a fine-tuned version of cardiffnlp/twitter-xlm-roberta-base-sentiment-multilingual on the all dataset. It achieves the following results on the evaluation set:

• Loss: 0.7405
• Precision: 0.6431
• Recall: 0.6554
• F1: 0.6401

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: 5e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 123
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 50.0
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1
0.6444	0.06	500	0.8771	0.6905	0.4537	0.4197
0.5499	0.12	1000	0.8167	0.7197	0.4260	0.4117
0.5357	0.18	1500	0.8084	0.7263	0.4696	0.4424
0.5175	0.24	2000	0.8704	0.6666	0.4266	0.3717
0.5285	0.3	2500	0.9067	0.7529	0.4565	0.4221
0.5081	0.36	3000	0.7414	0.7655	0.6114	0.6356
0.506	0.42	3500	0.8713	0.5830	0.6591	0.5786
0.5049	0.48	4000	0.7514	0.5551	0.4568	0.4464
0.4999	0.54	4500	0.7584	0.6519	0.5502	0.5767
0.507	0.6	5000	0.8072	0.6479	0.5626	0.5636
0.5048	0.66	5500	0.8080	0.6260	0.5725	0.5730
0.4907	0.72	6000	0.7966	0.6976	0.5138	0.5224
0.493	0.78	6500	0.8193	0.7099	0.4949	0.4922
0.4668	0.84	7000	0.7502	0.6282	0.6942	0.6501
0.4717	0.9	7500	0.7636	0.6372	0.5109	0.5191
0.4774	0.96	8000	0.7652	0.7513	0.5360	0.5587
0.4676	1.02	8500	0.8482	0.6372	0.5918	0.5836
0.4361	1.08	9000	0.7456	0.6687	0.5177	0.5175
0.4536	1.14	9500	0.8449	0.7363	0.5160	0.5156
0.4277	1.2	10000	0.8648	0.6382	0.5247	0.5173
0.4444	1.26	10500	0.8723	0.5871	0.6622	0.5959
0.4269	1.32	11000	0.7856	0.6151	0.5521	0.5526
0.4322	1.38	11500	0.7405	0.6431	0.6554	0.6401
0.4435	1.44	12000	0.7682	0.6568	0.5751	0.5923
0.4429	1.5	12500	0.8824	0.5956	0.6006	0.5545
0.4381	1.56	13000	0.7879	0.4457	0.4727	0.4395
0.4389	1.62	13500	0.7555	0.6260	0.6984	0.6502
0.4529	1.68	14000	0.7981	0.6621	0.5546	0.5663
0.4509	1.74	14500	0.7827	0.6160	0.6321	0.6172
0.4413	1.8	15000	0.7895	0.6381	0.6357	0.6285
0.4198	1.86	15500	0.8345	0.5940	0.5526	0.5602
0.4415	1.92	16000	0.8746	0.6615	0.6612	0.6459
0.443	1.98	16500	0.8155	0.6516	0.5265	0.5352
0.4068	2.04	17000	0.7642	0.5838	0.6220	0.5975
0.3905	2.1	17500	0.7929	0.6720	0.5555	0.5740
0.3969	2.16	18000	0.8949	0.5330	0.4771	0.4687
0.3841	2.22	18500	0.9233	0.6028	0.5410	0.5492
0.4031	2.28	19000	0.7720	0.6089	0.5719	0.5776
0.3878	2.34	19500	0.9046	0.6265	0.5358	0.5318
0.4001	2.41	20000	0.8451	0.6960	0.5622	0.5761
0.3997	2.47	20500	0.8964	0.6170	0.5665	0.5541
0.3945	2.53	21000	0.8001	0.5553	0.5180	0.5195
0.4005	2.59	21500	0.8357	0.5519	0.5100	0.5170
0.3907	2.65	22000	0.8017	0.5884	0.5409	0.5552
0.3858	2.71	22500	0.8283	0.6036	0.5792	0.5862
0.3973	2.77	23000	0.9024	0.5770	0.5665	0.5393
0.3969	2.83	23500	0.8341	0.5642	0.5528	0.5558
0.3911	2.89	24000	0.8966	0.6045	0.5088	0.5070
0.3856	2.95	24500	0.8349	0.6021	0.5586	0.5689
0.3961	3.01	25000	0.9364	0.6119	0.5412	0.5585
0.3301	3.07	25500	0.9542	0.5757	0.6084	0.5813
0.3385	3.13	26000	1.0137	0.5563	0.5294	0.5346
0.3475	3.19	26500	0.9311	0.6359	0.5675	0.5822

Framework versions

• Transformers 4.38.2
• Pytorch 2.2.1+cu121
• Datasets 2.18.0
• Tokenizers 0.15.2