Error code:   JWTInvalidSignature
Exception:    InvalidSignatureError
Message:      Signature verification failed
Traceback:    Traceback (most recent call last):
                File "/src/libs/libapi/src/libapi/jwt_token.py", line 286, in validate_jwt
                  decoded = jwt.decode(
                      jwt=token,
                  ...<2 lines>...
                      options=options,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 368, in decode
                  decoded = self.decode_complete(
                      jwt,
                  ...<8 lines>...
                      leeway=leeway,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 265, in decode_complete
                  decoded = self._jws.decode_complete(
                      jwt,
                  ...<3 lines>...
                      detached_payload=detached_payload,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 270, in decode_complete
                  self._verify_signature(
                  ~~~~~~~~~~~~~~~~~~~~~~^
                      signing_input,
                      ^^^^^^^^^^^^^^
                  ...<4 lines>...
                      options=merged_options,
                      ^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 417, in _verify_signature
                  raise InvalidSignatureError("Signature verification failed")
              jwt.exceptions.InvalidSignatureError: Signature verification failed

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Document OCR using dots.mocr

This dataset contains OCR results from images in davanstrien/ufo-ColPali using dots.mocr, a 3B multilingual model with SOTA document parsing and SVG generation.

Processing Details

Source Dataset: davanstrien/ufo-ColPali
Model: rednote-hilab/dots.mocr
Number of Samples: 3
Processing Time: 2.0 min
Processing Date: 2026-03-19 17:38 UTC

Configuration

Image Column: image
Output Column: markdown
Dataset Split: train
Batch Size: 16
Prompt Mode: ocr
Max Model Length: 24,000 tokens
Max Output Tokens: 24,000
GPU Memory Utilization: 90.0%

Model Information

dots.mocr is a 3B multilingual document parsing model that excels at:

100+ Languages — Multilingual document support
Table extraction — Structured data recognition
Formulas — Mathematical notation preservation
Layout-aware — Reading order and structure preservation
Web screen parsing — Webpage layout analysis
Scene text spotting — Text detection in natural scenes
SVG code generation — Charts, UI layouts, scientific figures to SVG

Dataset Structure

The dataset contains all original columns plus:

markdown: The extracted text in markdown format
inference_info: JSON list tracking all OCR models applied to this dataset

Usage

from datasets import load_dataset
import json

# Load the dataset
dataset = load_dataset("{output_dataset_id}", split="train")

# Access the markdown text
for example in dataset:
    print(example["markdown"])
    break

# View all OCR models applied to this dataset
inference_info = json.loads(dataset[0]["inference_info"])
for info in inference_info:
    print(f"Column: {info['column_name']} - Model: {info['model_id']}")

Reproduction

This dataset was generated using the uv-scripts/ocr dots.mocr script:

uv run https://huggingface.co/datasets/uv-scripts/ocr/raw/main/dots-mocr.py \
    davanstrien/ufo-ColPali \
    <output-dataset> \
    --image-column image \
    --batch-size 16 \
    --prompt-mode ocr \
    --max-model-len 24000 \
    --max-tokens 24000 \
    --gpu-memory-utilization 0.9

Generated with UV Scripts

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