README.md

metadata

language:
  - en
license: mit
tags:
  - generated_from_trainer
  - deberta-v3
datasets:
  - glue
metrics:
  - accuracy
model-index:
  - name: ds_results
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: GLUE MNLI
          type: glue
          args: mnli
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.874593165174939

DeBERTa v3 (small) fine-tuned on MNLI

This model is a fine-tuned version of microsoft/deberta-v3-small on the GLUE MNLI dataset. It achieves the following results on the evaluation set:

• Loss: 0.4985
• Accuracy: 0.8746

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: 3e-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
• lr_scheduler_warmup_steps: 1000
• num_epochs: 3.0

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.7773	0.04	1000	0.5241	0.7984
0.546	0.08	2000	0.4629	0.8194
0.5032	0.12	3000	0.4704	0.8274
0.4711	0.16	4000	0.4383	0.8355
0.473	0.2	5000	0.4652	0.8305
0.4619	0.24	6000	0.4234	0.8386
0.4542	0.29	7000	0.4825	0.8349
0.4468	0.33	8000	0.3985	0.8513
0.4288	0.37	9000	0.4084	0.8493
0.4354	0.41	10000	0.3850	0.8533
0.423	0.45	11000	0.3855	0.8509
0.4167	0.49	12000	0.4122	0.8513
0.4129	0.53	13000	0.4009	0.8550
0.4135	0.57	14000	0.4136	0.8544
0.4074	0.61	15000	0.3869	0.8595
0.415	0.65	16000	0.3911	0.8517
0.4095	0.69	17000	0.3880	0.8593
0.4001	0.73	18000	0.3907	0.8587
0.4069	0.77	19000	0.3686	0.8630
0.3927	0.81	20000	0.4008	0.8593
0.3958	0.86	21000	0.3716	0.8639
0.4016	0.9	22000	0.3594	0.8679
0.3945	0.94	23000	0.3595	0.8679
0.3932	0.98	24000	0.3577	0.8645
0.345	1.02	25000	0.4080	0.8699
0.2885	1.06	26000	0.3919	0.8674
0.2858	1.1	27000	0.4346	0.8651
0.2872	1.14	28000	0.4105	0.8674
0.3002	1.18	29000	0.4133	0.8708
0.2954	1.22	30000	0.4062	0.8667
0.2912	1.26	31000	0.3972	0.8708
0.2958	1.3	32000	0.3713	0.8732
0.293	1.34	33000	0.3717	0.8715
0.3001	1.39	34000	0.3826	0.8716
0.2864	1.43	35000	0.4155	0.8694
0.2827	1.47	36000	0.4224	0.8666
0.2836	1.51	37000	0.3832	0.8744
0.2844	1.55	38000	0.4179	0.8699
0.2866	1.59	39000	0.3969	0.8681
0.2883	1.63	40000	0.4000	0.8683
0.2832	1.67	41000	0.3853	0.8688
0.2876	1.71	42000	0.3924	0.8677
0.2855	1.75	43000	0.4177	0.8719
0.2845	1.79	44000	0.3877	0.8724
0.2882	1.83	45000	0.3961	0.8713
0.2773	1.87	46000	0.3791	0.8740
0.2767	1.91	47000	0.3877	0.8779
0.2772	1.96	48000	0.4022	0.8690
0.2816	2.0	49000	0.3837	0.8732
0.2068	2.04	50000	0.4644	0.8720
0.1914	2.08	51000	0.4919	0.8744
0.2	2.12	52000	0.4870	0.8702
0.1904	2.16	53000	0.5038	0.8737
0.1915	2.2	54000	0.5232	0.8711
0.1956	2.24	55000	0.5192	0.8747
0.1911	2.28	56000	0.5215	0.8761
0.2053	2.32	57000	0.4604	0.8738
0.2008	2.36	58000	0.5162	0.8715
0.1971	2.4	59000	0.4886	0.8754
0.192	2.44	60000	0.4921	0.8725
0.1937	2.49	61000	0.4917	0.8763
0.1931	2.53	62000	0.4789	0.8778
0.1964	2.57	63000	0.4997	0.8721
0.2008	2.61	64000	0.4748	0.8756
0.1962	2.65	65000	0.4840	0.8764
0.2029	2.69	66000	0.4889	0.8767
0.1927	2.73	67000	0.4820	0.8758
0.1926	2.77	68000	0.4857	0.8762
0.1919	2.81	69000	0.4836	0.8749
0.1911	2.85	70000	0.4859	0.8742
0.1897	2.89	71000	0.4853	0.8766
0.186	2.93	72000	0.4946	0.8768
0.2011	2.97	73000	0.4851	0.8767

Framework versions

• Transformers 4.13.0.dev0
• Pytorch 1.10.0+cu111
• Datasets 1.15.1
• Tokenizers 0.10.3