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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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datasets: |
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- crows_pairs |
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metrics: |
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- accuracy |
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model-index: |
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- name: xlnet-base-cased_crows_pairs_finetuned |
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results: |
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- task: |
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name: Text Classification |
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type: text-classification |
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dataset: |
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name: crows_pairs |
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type: crows_pairs |
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config: crows_pairs |
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split: test |
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args: crows_pairs |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.5 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# xlnet-base-cased_crows_pairs_finetuned |
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This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the crows_pairs dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6933 |
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- Accuracy: 0.5 |
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- Tp: 0.5 |
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- Tn: 0.0 |
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- Fp: 0.5 |
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- Fn: 0.0 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0001 |
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- train_batch_size: 64 |
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- eval_batch_size: 64 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 50 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Tp | Tn | Fp | Fn | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:------:|:------:| |
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| 0.7406 | 1.05 | 20 | 0.6941 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 | |
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| 0.7008 | 2.11 | 40 | 0.6959 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 | |
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| 0.7067 | 3.16 | 60 | 0.6932 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 | |
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| 0.7029 | 4.21 | 80 | 0.6937 | 0.5 | 0.0 | 0.5 | 0.0 | 0.5 | |
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| 0.7103 | 5.26 | 100 | 0.6932 | 0.5 | 0.0 | 0.5 | 0.0 | 0.5 | |
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| 0.7085 | 6.32 | 120 | 0.7004 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 | |
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| 0.7061 | 7.37 | 140 | 0.6933 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 | |
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| 0.7013 | 8.42 | 160 | 0.6954 | 0.5 | 0.0 | 0.5 | 0.0 | 0.5 | |
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| 0.6952 | 9.47 | 180 | 0.6933 | 0.5 | 0.0 | 0.5 | 0.0 | 0.5 | |
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| 0.7084 | 10.53 | 200 | 0.7079 | 0.5 | 0.0 | 0.5 | 0.0 | 0.5 | |
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| 0.71 | 11.58 | 220 | 0.6999 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 | |
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| 0.7036 | 12.63 | 240 | 0.6932 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 | |
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| 0.7043 | 13.68 | 260 | 0.6942 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 | |
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| 0.7058 | 14.74 | 280 | 0.6947 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 | |
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| 0.6993 | 15.79 | 300 | 0.6951 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 | |
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| 0.7009 | 16.84 | 320 | 0.6936 | 0.5 | 0.0 | 0.5 | 0.0 | 0.5 | |
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| 0.7069 | 17.89 | 340 | 0.7002 | 0.5 | 0.0 | 0.5 | 0.0 | 0.5 | |
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| 0.7068 | 18.95 | 360 | 0.6970 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 | |
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| 0.7042 | 20.0 | 380 | 0.6935 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 | |
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| 0.6999 | 21.05 | 400 | 0.6957 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 | |
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| 0.6966 | 22.11 | 420 | 0.6936 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 | |
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| 0.6975 | 23.16 | 440 | 0.6934 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 | |
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| 0.7043 | 24.21 | 460 | 0.6934 | 0.5 | 0.0 | 0.5 | 0.0 | 0.5 | |
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| 0.7002 | 25.26 | 480 | 0.6932 | 0.5 | 0.0 | 0.5 | 0.0 | 0.5 | |
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| 0.7039 | 26.32 | 500 | 0.7004 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 | |
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| 0.6927 | 27.37 | 520 | 0.6932 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 | |
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| 0.7078 | 28.42 | 540 | 0.6941 | 0.5 | 0.0 | 0.5 | 0.0 | 0.5 | |
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| 0.6999 | 29.47 | 560 | 0.6969 | 0.5 | 0.0 | 0.5 | 0.0 | 0.5 | |
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| 0.7063 | 30.53 | 580 | 0.6936 | 0.5 | 0.0 | 0.5 | 0.0 | 0.5 | |
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| 0.7011 | 31.58 | 600 | 0.6934 | 0.5 | 0.0 | 0.5 | 0.0 | 0.5 | |
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| 0.7061 | 32.63 | 620 | 0.6958 | 0.5 | 0.0 | 0.5 | 0.0 | 0.5 | |
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| 0.6971 | 33.68 | 640 | 0.6932 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 | |
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| 0.7007 | 34.74 | 660 | 0.6932 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 | |
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| 0.7014 | 35.79 | 680 | 0.6954 | 0.5 | 0.0 | 0.5 | 0.0 | 0.5 | |
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| 0.6976 | 36.84 | 700 | 0.6951 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 | |
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| 0.6957 | 37.89 | 720 | 0.6936 | 0.5 | 0.0 | 0.5 | 0.0 | 0.5 | |
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| 0.7009 | 38.95 | 740 | 0.6950 | 0.5 | 0.0 | 0.5 | 0.0 | 0.5 | |
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| 0.6941 | 40.0 | 760 | 0.6933 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 | |
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| 0.6989 | 41.05 | 780 | 0.6948 | 0.5 | 0.0 | 0.5 | 0.0 | 0.5 | |
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| 0.6935 | 42.11 | 800 | 0.6974 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 | |
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| 0.6939 | 43.16 | 820 | 0.6956 | 0.5 | 0.0 | 0.5 | 0.0 | 0.5 | |
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| 0.6975 | 44.21 | 840 | 0.6955 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 | |
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| 0.669 | 45.26 | 860 | 0.7089 | 0.5132 | 0.1623 | 0.3510 | 0.1490 | 0.3377 | |
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| 0.6896 | 46.32 | 880 | 0.7088 | 0.4669 | 0.4106 | 0.0563 | 0.4437 | 0.0894 | |
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| 0.6942 | 47.37 | 900 | 0.6944 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 | |
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| 0.6942 | 48.42 | 920 | 0.6933 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 | |
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| 0.6921 | 49.47 | 940 | 0.6933 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 | |
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### Framework versions |
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- Transformers 4.26.1 |
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- Pytorch 1.13.1 |
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- Datasets 2.10.1 |
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- Tokenizers 0.13.2 |
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