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update model card README.md
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README.md
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---
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license: bsd-3-clause
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tags:
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- generated_from_trainer
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datasets:
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- imagefolder
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model-index:
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- name: models
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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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# models
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This model is a fine-tuned version of [Salesforce/blip-image-captioning-base](https://huggingface.co/Salesforce/blip-image-captioning-base) on the imagefolder dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.4107
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- Wer Score: 0.5495
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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: 1
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- eval_batch_size: 1
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- seed: 42
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 4
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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 | Wer Score |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|
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| 9.4536 | 0.05 | 10 | 7.8217 | 41.7753 |
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| 7.3267 | 0.11 | 20 | 6.6585 | 0.7753 |
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| 6.2358 | 0.16 | 30 | 5.7758 | 0.5667 |
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| 5.2862 | 0.22 | 40 | 4.7628 | 0.5419 |
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| 4.3786 | 0.27 | 50 | 3.9203 | 0.6398 |
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| 3.5554 | 0.33 | 60 | 3.1482 | 0.5613 |
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| 2.849 | 0.38 | 70 | 2.5209 | 0.5548 |
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| 2.3041 | 0.44 | 80 | 2.0561 | 0.5645 |
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| 1.8999 | 0.49 | 90 | 1.7474 | 0.5645 |
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| 1.658 | 0.55 | 100 | 1.5722 | 0.5548 |
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| 1.5238 | 0.6 | 110 | 1.4836 | 0.5591 |
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| 1.4726 | 0.66 | 120 | 1.4461 | 0.5538 |
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| 1.4328 | 0.71 | 130 | 1.4285 | 0.5473 |
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| 1.4211 | 0.77 | 140 | 1.4205 | 0.5559 |
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| 1.4202 | 0.82 | 150 | 1.4156 | 0.5548 |
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| 1.4098 | 0.88 | 160 | 1.4129 | 0.5505 |
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| 1.4124 | 0.93 | 170 | 1.4113 | 0.5548 |
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| 1.4075 | 0.99 | 180 | 1.4107 | 0.5495 |
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### Framework versions
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- Transformers 4.30.2
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- Pytorch 2.0.1+cpu
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- Datasets 2.13.1
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- Tokenizers 0.13.3
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