metadata

license: mit
base_model: xlm-roberta-base
tags:
  - generated_from_trainer
datasets:
  - tweet_sentiment_multilingual
metrics:
  - accuracy
  - f1
model-index:
  - name: >-
      scenario-NON-KD-SCR-COPY-CDF-ALL-D2_data-cardiffnlp_tweet_sentiment_multilingual
    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: 0.4972993827160494
          - name: F1
            type: f1
            value: 0.49564146924204383

scenario-NON-KD-SCR-COPY-CDF-ALL-D2_data-cardiffnlp_tweet_sentiment_multilingual

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: 5.7429
Accuracy: 0.4973
F1: 0.4956

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: 32
eval_batch_size: 32
seed: 222
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
1.1068	1.09	500	1.0681	0.4352	0.4321
0.9081	2.17	1000	1.2631	0.5046	0.5021
0.5532	3.26	1500	1.5304	0.5108	0.5089
0.2998	4.35	2000	2.0584	0.4884	0.4858
0.1717	5.43	2500	2.7362	0.5	0.4939
0.1242	6.52	3000	3.0470	0.4969	0.4938
0.0874	7.61	3500	2.7990	0.5046	0.5037
0.0669	8.7	4000	3.2793	0.4942	0.4940
0.056	9.78	4500	3.2094	0.5027	0.5028
0.0487	10.87	5000	3.5054	0.4992	0.4972
0.0539	11.96	5500	3.2798	0.5008	0.5003
0.0317	13.04	6000	3.4251	0.5004	0.4994
0.0449	14.13	6500	4.0353	0.4969	0.4923
0.0303	15.22	7000	4.3157	0.4850	0.4733
0.0285	16.3	7500	3.8740	0.4985	0.4987
0.0214	17.39	8000	4.5553	0.4842	0.4828
0.0228	18.48	8500	4.7444	0.4946	0.4903
0.0177	19.57	9000	4.5373	0.4969	0.4939
0.0167	20.65	9500	4.4792	0.4927	0.4859
0.0144	21.74	10000	4.6491	0.4896	0.4897
0.0164	22.83	10500	4.8310	0.4934	0.4926
0.0116	23.91	11000	4.6267	0.4996	0.4965
0.0102	25.0	11500	5.0420	0.4904	0.4808
0.0053	26.09	12000	5.2202	0.4915	0.4824
0.01	27.17	12500	4.8786	0.4900	0.4868
0.0076	28.26	13000	4.8830	0.4919	0.4906
0.0064	29.35	13500	5.2319	0.4934	0.4890
0.0055	30.43	14000	5.4810	0.4973	0.4953
0.0057	31.52	14500	5.4109	0.5035	0.5019
0.0032	32.61	15000	5.3979	0.5054	0.5041
0.0092	33.7	15500	5.3848	0.4942	0.4940
0.0053	34.78	16000	5.2937	0.5066	0.5046
0.0029	35.87	16500	5.5430	0.5012	0.4971
0.0011	36.96	17000	5.6338	0.4919	0.4905
0.0027	38.04	17500	5.6234	0.4958	0.4960
0.0042	39.13	18000	5.5802	0.4988	0.4991
0.0012	40.22	18500	5.6464	0.4988	0.4993
0.0037	41.3	19000	5.6227	0.4965	0.4945
0.0007	42.39	19500	5.6263	0.4958	0.4939
0.0003	43.48	20000	5.6946	0.4934	0.4937
0.0016	44.57	20500	5.6654	0.4973	0.4977
0.0018	45.65	21000	5.6725	0.4965	0.4952
0.0012	46.74	21500	5.6500	0.4873	0.4869
0.0008	47.83	22000	5.6626	0.4992	0.4985
0.0006	48.91	22500	5.7378	0.4985	0.4968
0.0004	50.0	23000	5.7429	0.4973	0.4956

Framework versions

Transformers 4.33.3
Pytorch 2.1.1+cu121
Datasets 2.14.5
Tokenizers 0.13.3