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_d
    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: 0.6396604938271605
          - name: F1
            type: f1
            value: 0.6384456793550767

scenario-TCR_data-cardiffnlp_tweet_sentiment_multilingual_all_d

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: 2.8506
Accuracy: 0.6397
F1: 0.6384

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: 53
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
0.9598	1.09	500	0.8321	0.6335	0.6229
0.7983	2.17	1000	0.7922	0.6381	0.6278
0.7031	3.26	1500	0.8300	0.6520	0.6468
0.6192	4.35	2000	0.8659	0.6497	0.6443
0.5472	5.43	2500	0.9646	0.6331	0.6343
0.4664	6.52	3000	0.9555	0.6485	0.6455
0.4025	7.61	3500	1.0121	0.6427	0.6405
0.3568	8.7	4000	1.1016	0.6327	0.6324
0.3069	9.78	4500	1.2521	0.6408	0.6400
0.2701	10.87	5000	1.3727	0.6397	0.6372
0.2398	11.96	5500	1.4539	0.6319	0.6334
0.2004	13.04	6000	1.6097	0.6420	0.6376
0.1864	14.13	6500	1.6302	0.6343	0.6349
0.157	15.22	7000	1.7491	0.6381	0.6339
0.1411	16.3	7500	1.8634	0.6400	0.6392
0.1318	17.39	8000	2.0229	0.6277	0.6275
0.1159	18.48	8500	2.0196	0.6385	0.6359
0.1135	19.57	9000	2.1959	0.6377	0.6368
0.1018	20.65	9500	2.3238	0.6323	0.6320
0.0888	21.74	10000	2.3449	0.6339	0.6341
0.0797	22.83	10500	2.4967	0.6354	0.6338
0.0828	23.91	11000	2.5070	0.6358	0.6362
0.0675	25.0	11500	2.5895	0.6381	0.6393
0.067	26.09	12000	2.6730	0.6370	0.6372
0.0566	27.17	12500	2.7454	0.6377	0.6386
0.0571	28.26	13000	2.7673	0.6420	0.6413
0.048	29.35	13500	2.8506	0.6397	0.6384

Framework versions

Transformers 4.33.3
Pytorch 2.1.1+cu121
Datasets 2.14.5
Tokenizers 0.13.3