YAML Metadata Warning: The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

Finetuned version of hmByT5 on DE1, DE2, DE3 and DE7 parts of the IDCAR2019-POCR dataset to correct OCR mistakes. The max_length was set to 350.

Performance

SacreBLEU eval dataset: 10.83 
SacreBLEU eval model: 72.35

Usage

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

example_sentence = "Anvpreiſungq. Haupidepot für Wien: In der Stadt, obere Bräunerſtraße Nr. 1137 in der Varfüͤmerie-Handlung zur"

tokenizer = AutoTokenizer.from_pretrained("Var3n/hmByT5_anno")
model = AutoModelForSeq2SeqLM.from_pretrained("Var3n/hmByT5_anno")

input = tokenizer(example_sentence, return_tensors="pt").input_ids
output = model.generate(input, max_new_tokens=len(input[0]), num_beams=4, do_sample=True)

text = tokenizer.decode(output[0], skip_special_tokens=True)

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