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Fine tuning BERT base for InfoVQA
cf6ab4e
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-base-uncased-finetuned-infovqa
results:
- task:
name: Question Answering
type: question-answering
---
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should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-infovqa
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8276
## 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: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 250500
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.2765 | 0.23 | 1000 | 3.0678 |
| 2.9987 | 0.46 | 2000 | 2.9525 |
| 2.826 | 0.69 | 3000 | 2.7870 |
| 2.7084 | 0.93 | 4000 | 2.7051 |
| 2.1286 | 1.16 | 5000 | 2.9286 |
| 2.0009 | 1.39 | 6000 | 3.1037 |
| 2.0323 | 1.62 | 7000 | 2.8567 |
| 1.9905 | 1.85 | 8000 | 2.8276 |
### Framework versions
- Transformers 4.10.0
- Pytorch 1.8.0+cu101
- Datasets 1.11.0
- Tokenizers 0.10.3