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--- |
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license: cc-by-nc-sa-4.0 |
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base_model: microsoft/layoutlmv2-base-uncased |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: results |
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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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# results |
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This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://huggingface.co/microsoft/layoutlmv2-base-uncased) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: nan |
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## Model description |
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This DocVQA model, built on the Layout LM v2 framework, represents an initial step in a series of |
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experimental models aimed at document visual question answering. It's the "medium" version in a planned series, |
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trained on a mid-sized dataset of 5k samples (split between training and test) over 20 epochs. |
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The training setup was modest, employing mixed precision (fp16), with manageable batch sizes and a |
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focused approach to learning rate adjustment (warmup steps and weight decay). Notably, this model was |
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trained without external reporting tools, emphasizing internal evaluation. As the first iteration in a |
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progressive series that will later include medium (5k samples) and large (50k samples) models, this |
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version serves as a foundational experiment, setting the stage for more extensive and complex models in the |
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future. |
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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: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 64 |
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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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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 20 |
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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 | 0.99 | 62 | 5.4841 | |
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| No log | 2.0 | 125 | 4.6253 | |
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| No log | 2.99 | 187 | 4.3093 | |
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| No log | 4.0 | 250 | 4.0361 | |
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| No log | 4.99 | 312 | 3.6892 | |
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| No log | 6.0 | 375 | 3.3862 | |
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| No log | 6.99 | 437 | 3.0017 | |
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| 4.3469 | 8.0 | 500 | nan | |
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| 4.3469 | 8.99 | 562 | nan | |
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| 4.3469 | 10.0 | 625 | nan | |
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| 4.3469 | 10.99 | 687 | nan | |
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| 4.3469 | 12.0 | 750 | nan | |
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| 4.3469 | 12.99 | 812 | nan | |
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| 4.3469 | 14.0 | 875 | nan | |
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| 4.3469 | 14.99 | 937 | nan | |
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| 21709.916 | 16.0 | 1000 | nan | |
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| 21709.916 | 16.99 | 1062 | nan | |
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| 21709.916 | 18.0 | 1125 | nan | |
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| 21709.916 | 18.99 | 1187 | nan | |
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| 21709.916 | 19.84 | 1240 | nan | |
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### Framework versions |
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- Transformers 4.34.1 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.10.1 |
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- Tokenizers 0.14.1 |
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