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--- |
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license: apache-2.0 |
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tags: |
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- text-classification |
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- generated_from_trainer |
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datasets: |
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- xnli |
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metrics: |
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- accuracy |
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model-index: |
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- name: xnli_m_bert_only_en_single_gpu |
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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: xnli |
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type: xnli |
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config: en |
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split: train |
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args: en |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.810843373493976 |
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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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# xnli_m_bert_only_en_single_gpu |
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This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the xnli dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5306 |
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- Accuracy: 0.8108 |
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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: 5e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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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: 3.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:| |
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| 0.8884 | 0.04 | 1000 | 0.7743 | 0.6703 | |
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| 0.782 | 0.08 | 2000 | 0.7029 | 0.7060 | |
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| 0.7479 | 0.12 | 3000 | 0.7366 | 0.6880 | |
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| 0.7348 | 0.16 | 4000 | 0.6722 | 0.7285 | |
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| 0.721 | 0.2 | 5000 | 0.6802 | 0.7237 | |
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| 0.7097 | 0.24 | 6000 | 0.6801 | 0.7217 | |
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| 0.6978 | 0.29 | 7000 | 0.6051 | 0.7643 | |
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| 0.6924 | 0.33 | 8000 | 0.6793 | 0.7357 | |
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| 0.6807 | 0.37 | 9000 | 0.6604 | 0.7502 | |
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| 0.6636 | 0.41 | 10000 | 0.6309 | 0.7430 | |
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| 0.6616 | 0.45 | 11000 | 0.6039 | 0.7490 | |
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| 0.6561 | 0.49 | 12000 | 0.6051 | 0.7610 | |
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| 0.6545 | 0.53 | 13000 | 0.6354 | 0.7454 | |
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| 0.644 | 0.57 | 14000 | 0.6064 | 0.7466 | |
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| 0.6446 | 0.61 | 15000 | 0.6052 | 0.7554 | |
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| 0.6414 | 0.65 | 16000 | 0.6365 | 0.7422 | |
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| 0.6311 | 0.69 | 17000 | 0.6118 | 0.7546 | |
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| 0.6187 | 0.73 | 18000 | 0.5973 | 0.7538 | |
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| 0.619 | 0.77 | 19000 | 0.5863 | 0.7570 | |
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| 0.6108 | 0.81 | 20000 | 0.6212 | 0.7490 | |
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| 0.6136 | 0.86 | 21000 | 0.5810 | 0.7695 | |
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| 0.6018 | 0.9 | 22000 | 0.5799 | 0.7731 | |
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| 0.6198 | 0.94 | 23000 | 0.5548 | 0.7723 | |
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| 0.6047 | 0.98 | 24000 | 0.5964 | 0.7622 | |
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| 0.5636 | 1.02 | 25000 | 0.5805 | 0.7851 | |
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| 0.5267 | 1.06 | 26000 | 0.5540 | 0.7795 | |
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| 0.5067 | 1.1 | 27000 | 0.5388 | 0.7855 | |
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| 0.5304 | 1.14 | 28000 | 0.5482 | 0.7799 | |
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| 0.5332 | 1.18 | 29000 | 0.5290 | 0.7859 | |
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| 0.5154 | 1.22 | 30000 | 0.5475 | 0.7799 | |
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| 0.524 | 1.26 | 31000 | 0.5305 | 0.7900 | |
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| 0.5236 | 1.3 | 32000 | 0.5691 | 0.7871 | |
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| 0.5154 | 1.34 | 33000 | 0.5642 | 0.7739 | |
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| 0.5248 | 1.39 | 34000 | 0.5590 | 0.7643 | |
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| 0.5077 | 1.43 | 35000 | 0.6064 | 0.7715 | |
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| 0.5147 | 1.47 | 36000 | 0.5343 | 0.7948 | |
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| 0.5041 | 1.51 | 37000 | 0.5375 | 0.7867 | |
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| 0.5054 | 1.55 | 38000 | 0.5660 | 0.7727 | |
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| 0.5053 | 1.59 | 39000 | 0.5479 | 0.7859 | |
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| 0.5009 | 1.63 | 40000 | 0.5080 | 0.7960 | |
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| 0.5081 | 1.67 | 41000 | 0.5139 | 0.7920 | |
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| 0.5013 | 1.71 | 42000 | 0.5385 | 0.7904 | |
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| 0.4972 | 1.75 | 43000 | 0.5257 | 0.7928 | |
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| 0.4987 | 1.79 | 44000 | 0.5056 | 0.8020 | |
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| 0.4863 | 1.83 | 45000 | 0.5030 | 0.8004 | |
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| 0.5 | 1.87 | 46000 | 0.5157 | 0.7980 | |
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| 0.4926 | 1.91 | 47000 | 0.5505 | 0.7924 | |
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| 0.4893 | 1.96 | 48000 | 0.5286 | 0.8004 | |
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| 0.4755 | 2.0 | 49000 | 0.5216 | 0.8036 | |
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| 0.3855 | 2.04 | 50000 | 0.6087 | 0.7884 | |
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| 0.3731 | 2.08 | 51000 | 0.5485 | 0.8064 | |
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| 0.3698 | 2.12 | 52000 | 0.5398 | 0.8080 | |
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| 0.3702 | 2.16 | 53000 | 0.5454 | 0.8 | |
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| 0.3688 | 2.2 | 54000 | 0.5512 | 0.8068 | |
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| 0.3683 | 2.24 | 55000 | 0.5423 | 0.8060 | |
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| 0.3704 | 2.28 | 56000 | 0.5383 | 0.8084 | |
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| 0.3758 | 2.32 | 57000 | 0.5398 | 0.8161 | |
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| 0.3781 | 2.36 | 58000 | 0.5481 | 0.8088 | |
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| 0.3697 | 2.4 | 59000 | 0.5465 | 0.8056 | |
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| 0.3706 | 2.44 | 60000 | 0.5488 | 0.7988 | |
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| 0.3704 | 2.49 | 61000 | 0.5395 | 0.8052 | |
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| 0.3648 | 2.53 | 62000 | 0.5463 | 0.8068 | |
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| 0.36 | 2.57 | 63000 | 0.5400 | 0.8052 | |
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| 0.3661 | 2.61 | 64000 | 0.5542 | 0.8068 | |
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| 0.3555 | 2.65 | 65000 | 0.5424 | 0.8044 | |
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| 0.3551 | 2.69 | 66000 | 0.5269 | 0.8124 | |
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| 0.3608 | 2.73 | 67000 | 0.5382 | 0.8129 | |
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| 0.35 | 2.77 | 68000 | 0.5461 | 0.8108 | |
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| 0.3457 | 2.81 | 69000 | 0.5477 | 0.8084 | |
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| 0.3516 | 2.85 | 70000 | 0.5345 | 0.8104 | |
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| 0.3499 | 2.89 | 71000 | 0.5344 | 0.8129 | |
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| 0.3513 | 2.93 | 72000 | 0.5279 | 0.8120 | |
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| 0.3442 | 2.97 | 73000 | 0.5306 | 0.8108 | |
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### Framework versions |
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- Transformers 4.24.0 |
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- Pytorch 1.13.0 |
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- Datasets 2.6.1 |
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- Tokenizers 0.13.1 |
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