Instructions to use chanelcolgate/trocr-base-printed_captcha_ocr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chanelcolgate/trocr-base-printed_captcha_ocr with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "image-to-text" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("image-to-text", model="chanelcolgate/trocr-base-printed_captcha_ocr")# Load model directly from transformers import AutoTokenizer, AutoModelForImageTextToText tokenizer = AutoTokenizer.from_pretrained("chanelcolgate/trocr-base-printed_captcha_ocr") model = AutoModelForImageTextToText.from_pretrained("chanelcolgate/trocr-base-printed_captcha_ocr") - Notebooks
- Google Colab
- Kaggle
trocr-base-printed_captcha_ocr
This model is a fine-tuned version of microsoft/trocr-base-printed on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0888
- Cer: 0.0034
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Cer |
|---|---|---|---|---|
| 0.6822 | 1.0 | 750 | 0.2687 | 0.0418 |
| 0.1595 | 2.0 | 1500 | 0.1413 | 0.0094 |
| 0.0481 | 3.0 | 2250 | 0.0717 | 0.0029 |
Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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Model tree for chanelcolgate/trocr-base-printed_captcha_ocr
Base model
microsoft/trocr-base-printed