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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: distilbert-base-uncased-finetuned-infovqa |
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results: [] |
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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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# distilbert-base-uncased-finetuned-infovqa |
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.8872 |
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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: 4 |
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- eval_batch_size: 4 |
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- seed: 250500 |
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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: 1 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| No log | 0.02 | 100 | 4.7706 | |
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| No log | 0.05 | 200 | 4.4399 | |
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| No log | 0.07 | 300 | 3.8175 | |
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| No log | 0.09 | 400 | 3.8306 | |
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| 3.3071 | 0.12 | 500 | 3.6480 | |
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| 3.3071 | 0.14 | 600 | 3.6451 | |
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| 3.3071 | 0.16 | 700 | 3.4974 | |
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| 3.3071 | 0.19 | 800 | 3.4686 | |
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| 3.3071 | 0.21 | 900 | 3.4703 | |
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| 3.5336 | 0.23 | 1000 | 3.3165 | |
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| 3.5336 | 0.25 | 1100 | 3.3634 | |
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| 3.5336 | 0.28 | 1200 | 3.3466 | |
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| 3.5336 | 0.3 | 1300 | 3.3411 | |
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| 3.5336 | 0.32 | 1400 | 3.2456 | |
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| 3.3593 | 0.35 | 1500 | 3.3257 | |
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| 3.3593 | 0.37 | 1600 | 3.2941 | |
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| 3.3593 | 0.39 | 1700 | 3.2581 | |
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| 3.3593 | 0.42 | 1800 | 3.1680 | |
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| 3.3593 | 0.44 | 1900 | 3.2077 | |
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| 3.2436 | 0.46 | 2000 | 3.2422 | |
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| 3.2436 | 0.49 | 2100 | 3.2529 | |
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| 3.2436 | 0.51 | 2200 | 3.2681 | |
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| 3.2436 | 0.53 | 2300 | 3.1055 | |
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| 3.2436 | 0.56 | 2400 | 3.0174 | |
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| 3.093 | 0.58 | 2500 | 3.0608 | |
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| 3.093 | 0.6 | 2600 | 3.0200 | |
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| 3.093 | 0.63 | 2700 | 2.9884 | |
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| 3.093 | 0.65 | 2800 | 3.0041 | |
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| 3.093 | 0.67 | 2900 | 2.9700 | |
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| 3.0087 | 0.69 | 3000 | 3.0993 | |
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| 3.0087 | 0.72 | 3100 | 3.0499 | |
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| 3.0087 | 0.74 | 3200 | 2.9317 | |
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| 3.0087 | 0.76 | 3300 | 3.0817 | |
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| 3.0087 | 0.79 | 3400 | 3.0035 | |
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| 2.9694 | 0.81 | 3500 | 3.0850 | |
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| 2.9694 | 0.83 | 3600 | 2.9948 | |
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| 2.9694 | 0.86 | 3700 | 2.9874 | |
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| 2.9694 | 0.88 | 3800 | 2.9202 | |
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| 2.9694 | 0.9 | 3900 | 2.9322 | |
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| 2.8277 | 0.93 | 4000 | 2.9195 | |
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| 2.8277 | 0.95 | 4100 | 2.8638 | |
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| 2.8277 | 0.97 | 4200 | 2.8809 | |
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| 2.8277 | 1.0 | 4300 | 2.8872 | |
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### Framework versions |
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- Transformers 4.11.3 |
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- Pytorch 1.9.0+cu111 |
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- Datasets 1.14.0 |
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- Tokenizers 0.10.3 |
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