t5gemma2-latex-ocr-1k

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t5gemma2-latex-ocr-1k

This model is a fine-tuned version of google/t5gemma-2-270m-270m on an unknown dataset.

Usage

from transformers import pipeline

generator = pipeline(
    "image-text-to-text",
    model="kingabzpro/t5gemma2-latex-ocr-1k",
)

generator(
    val_ds[10]["image"],
    text="<start_of_image> Convert this image to LaTeX. Output only LaTeX.",
    generate_kwargs={"do_sample": False, "max_new_tokens": 100},
)

Output:

[{'input_text': '<start_of_image> Convert this image to LaTeX. Output only LaTeX.',
  'generated_text': '<start_of_image> Convert this image to LaTeX. Output only LaTeX.f ( p , p ^ { \\prime } ) = \\ln \\left\\{ \\begin{array} { \\begin{array} { \\begin{array} { \\begin{array} { \\begin{array} { \\end{array} \\right\\} \\begin{array} { \\begin{array} { \\begin{array} { \\end{array} \\right\\} \\begin{array} { \\begin{array} { \\begin{array} {'}]

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0001
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 15
• num_epochs: 1

Framework versions

• Transformers 5.0.0rc1
• Pytorch 2.8.0+cu128
• Datasets 4.4.2
• Tokenizers 0.22.1