README.md

metadata

license: mit
tags:
  - generated_from_trainer
datasets:
  - rotten_tomatoes
metrics:
  - accuracy
model-index:
  - name: rtm_roBERTa_5E
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: rotten_tomatoes
          type: rotten_tomatoes
          config: default
          split: train
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.8666666666666667

rtm_roBERTa_5E

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

• Loss: 0.6545
• Accuracy: 0.8667

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: 16
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.6955	0.09	50	0.6752	0.7867
0.5362	0.19	100	0.4314	0.8333
0.4065	0.28	150	0.4476	0.8533
0.3563	0.37	200	0.3454	0.8467
0.3729	0.47	250	0.3421	0.86
0.3355	0.56	300	0.3253	0.8467
0.338	0.66	350	0.3859	0.8733
0.2875	0.75	400	0.3537	0.8533
0.3477	0.84	450	0.3636	0.8467
0.3259	0.94	500	0.3115	0.88
0.3204	1.03	550	0.4295	0.8333
0.2673	1.12	600	0.3369	0.88
0.2479	1.22	650	0.3620	0.8667
0.2821	1.31	700	0.3582	0.8733
0.2355	1.4	750	0.3130	0.8867
0.2357	1.5	800	0.3229	0.86
0.2725	1.59	850	0.3035	0.88
0.2425	1.69	900	0.3146	0.8533
0.1977	1.78	950	0.4079	0.86
0.2557	1.87	1000	0.4132	0.8733
0.2395	1.97	1050	0.3336	0.86
0.1951	2.06	1100	0.5068	0.84
0.1631	2.15	1150	0.5209	0.8867
0.2192	2.25	1200	0.4766	0.8733
0.1725	2.34	1250	0.3962	0.8667
0.2215	2.43	1300	0.4133	0.8867
0.1602	2.53	1350	0.5564	0.8533
0.1986	2.62	1400	0.5826	0.86
0.1972	2.72	1450	0.5412	0.8667
0.2299	2.81	1500	0.4636	0.8733
0.2028	2.9	1550	0.5096	0.8667
0.2591	3.0	1600	0.3790	0.8467
0.1197	3.09	1650	0.5704	0.8467
0.174	3.18	1700	0.5904	0.8467
0.1499	3.28	1750	0.6066	0.86
0.1687	3.37	1800	0.6353	0.8533
0.1463	3.46	1850	0.6434	0.8467
0.1373	3.56	1900	0.6507	0.8533
0.1339	3.65	1950	0.6014	0.86
0.1488	3.75	2000	0.7245	0.84
0.1725	3.84	2050	0.6214	0.86
0.1443	3.93	2100	0.6446	0.8533
0.1619	4.03	2150	0.6223	0.8533
0.1153	4.12	2200	0.6579	0.8333
0.1159	4.21	2250	0.6760	0.8667
0.0948	4.31	2300	0.7172	0.8467
0.1373	4.4	2350	0.7346	0.8467
0.1463	4.49	2400	0.6453	0.8533
0.0758	4.59	2450	0.6579	0.86
0.16	4.68	2500	0.6556	0.8667
0.112	4.78	2550	0.6490	0.88
0.1151	4.87	2600	0.6525	0.8667
0.2152	4.96	2650	0.6545	0.8667

Framework versions

• Transformers 4.24.0
• Pytorch 1.13.0
• Datasets 2.7.1
• Tokenizers 0.13.2