metadata

license: mit
base_model: xlm-roberta-base
tags:
  - generated_from_trainer
datasets:
  - tweet_sentiment_multilingual
metrics:
  - accuracy
  - f1
model-index:
  - name: scenario-TCR_data-cardiffnlp_tweet_sentiment_multilingual_all_c
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: tweet_sentiment_multilingual
          type: tweet_sentiment_multilingual
          config: all
          split: validation
          args: all
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.6431327160493827
          - name: F1
            type: f1
            value: 0.6424433208447596

scenario-TCR_data-cardiffnlp_tweet_sentiment_multilingual_all_c

This model is a fine-tuned version of xlm-roberta-base on the tweet_sentiment_multilingual dataset. It achieves the following results on the evaluation set:

Loss: 2.5108
Accuracy: 0.6431
F1: 0.6424

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: 1e-05
train_batch_size: 32
eval_batch_size: 32
seed: 134
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
0.9471	1.09	500	0.8205	0.6412	0.6387
0.7916	2.17	1000	0.8077	0.6474	0.6462
0.6978	3.26	1500	0.8621	0.6528	0.6534
0.6176	4.35	2000	0.9091	0.6412	0.6363
0.5422	5.43	2500	0.9120	0.6454	0.6440
0.4822	6.52	3000	0.9097	0.6512	0.6469
0.4117	7.61	3500	1.0223	0.6420	0.6406
0.3669	8.7	4000	1.1259	0.6404	0.6427
0.3229	9.78	4500	1.2050	0.6516	0.6489
0.2797	10.87	5000	1.2616	0.6408	0.6415
0.2657	11.96	5500	1.3181	0.6435	0.6412
0.226	13.04	6000	1.4459	0.6400	0.6424
0.2123	14.13	6500	1.5978	0.6389	0.6379
0.1853	15.22	7000	1.6409	0.6412	0.6438
0.1759	16.3	7500	1.6756	0.6485	0.6495
0.1579	17.39	8000	1.6652	0.6412	0.6418
0.1409	18.48	8500	1.9476	0.6389	0.6384
0.1282	19.57	9000	2.0246	0.6285	0.6280
0.1254	20.65	9500	1.9803	0.6412	0.6437
0.1077	21.74	10000	2.0991	0.6447	0.6429
0.097	22.83	10500	2.1971	0.6424	0.6413
0.0965	23.91	11000	2.2161	0.6420	0.6387
0.0859	25.0	11500	2.3387	0.6346	0.6329
0.0744	26.09	12000	2.3921	0.6466	0.6458
0.0693	27.17	12500	2.4696	0.6424	0.6428
0.072	28.26	13000	2.5027	0.6435	0.6431
0.0701	29.35	13500	2.5108	0.6431	0.6424

Framework versions

Transformers 4.33.3
Pytorch 2.1.1+cu121
Datasets 2.14.5
Tokenizers 0.13.3