mamba2-780m-hf
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This model is a fine-tuned version of microsoft/trocr-base-printed on your custom dataset. captchas like processor = TrOCRProcessor.from_pretrained("anuashok/ocr-captcha-v2") model = VisionEncoderDecoderModel.from_pretrained("anuashok/ocr-captcha-v2") generated_ids = model.generate(pixel_values) generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] print(generated_text)
This model is a fine-tuned version of microsoft/trocr-base-printed on Captchas of the type shown below - **CER (Character Error Rate)**: 0.01394585726004922 - **Hyperparameters**: - **Learning Rate**: 1.5078922700531405e-05 - **Batch Size**: 16 - **Num Epochs**: 7 - **Warmup Ratio**: 0.14813004670666596 - **Weight Decay**: 0.017176551931326833 - **Num Beams**: 2 - **Length Penalty**: 1.3612823161368288 ```python from transformers import VisionEncoderDecoderModel, TrOCRProcessor import torch from PIL import Image
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Fine-tuned facebook/wav2vec2-large-xlsr-53 on Telugu using the OpenSLR SLR66 dataset. When using this model, make sure that your speech input is sampled at 16kHz. **Test Result**: 44.98% 70% of the OpenSLR Telugu dataset was used for training. Train Split of annotations is here
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