olmocr/olmocr.egg-info/PKG-INFO

Metadata-Version: 2.4
Name: olmocr
Version: 0.1.67
Author-email: Allen Institute for Artificial Intelligence <jakep@allenai.org>
License:                                  Apache License
                                   Version 2.0, January 2004
                                https://www.apache.org/licenses/
        
           TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
        
           1. Definitions.
        
              "License" shall mean the terms and conditions for use, reproduction,
              and distribution as defined by Sections 1 through 9 of this document.
        
              "Licensor" shall mean the copyright owner or entity authorized by
              the copyright owner that is granting the License.
        
              "Legal Entity" shall mean the union of the acting entity and all
              other entities that control, are controlled by, or are under common
              control with that entity. For the purposes of this definition,
              "control" means (i) the power, direct or indirect, to cause the
              direction or management of such entity, whether by contract or
              otherwise, or (ii) ownership of fifty percent (50%) or more of the
              outstanding shares, or (iii) beneficial ownership of such entity.
        
              "You" (or "Your") shall mean an individual or Legal Entity
              exercising permissions granted by this License.
        
              "Source" form shall mean the preferred form for making modifications,
              including but not limited to software source code, documentation
              source, and configuration files.
        
              "Object" form shall mean any form resulting from mechanical
              transformation or translation of a Source form, including but
              not limited to compiled object code, generated documentation,
              and conversions to other media types.
        
              "Work" shall mean the work of authorship, whether in Source or
              Object form, made available under the License, as indicated by a
              copyright notice that is included in or attached to the work
              (an example is provided in the Appendix below).
        
              "Derivative Works" shall mean any work, whether in Source or Object
              form, that is based on (or derived from) the Work and for which the
              editorial revisions, annotations, elaborations, or other modifications
              represent, as a whole, an original work of authorship. For the purposes
              of this License, Derivative Works shall not include works that remain
              separable from, or merely link (or bind by name) to the interfaces of,
              the Work and Derivative Works thereof.
        
              "Contribution" shall mean any work of authorship, including
              the original version of the Work and any modifications or additions
              to that Work or Derivative Works thereof, that is intentionally
              submitted to Licensor for inclusion in the Work by the copyright owner
              or by an individual or Legal Entity authorized to submit on behalf of
              the copyright owner. For the purposes of this definition, "submitted"
              means any form of electronic, verbal, or written communication sent
              to the Licensor or its representatives, including but not limited to
              communication on electronic mailing lists, source code control systems,
              and issue tracking systems that are managed by, or on behalf of, the
              Licensor for the purpose of discussing and improving the Work, but
              excluding communication that is conspicuously marked or otherwise
              designated in writing by the copyright owner as "Not a Contribution."
        
              "Contributor" shall mean Licensor and any individual or Legal Entity
              on behalf of whom a Contribution has been received by Licensor and
              subsequently incorporated within the Work.
        
           2. Grant of Copyright License. Subject to the terms and conditions of
              this License, each Contributor hereby grants to You a perpetual,
              worldwide, non-exclusive, no-charge, royalty-free, irrevocable
              copyright license to reproduce, prepare Derivative Works of,
              publicly display, publicly perform, sublicense, and distribute the
              Work and such Derivative Works in Source or Object form.
        
           3. Grant of Patent License. Subject to the terms and conditions of
              this License, each Contributor hereby grants to You a perpetual,
              worldwide, non-exclusive, no-charge, royalty-free, irrevocable
              (except as stated in this section) patent license to make, have made,
              use, offer to sell, sell, import, and otherwise transfer the Work,
              where such license applies only to those patent claims licensable
              by such Contributor that are necessarily infringed by their
              Contribution(s) alone or by combination of their Contribution(s)
              with the Work to which such Contribution(s) was submitted. If You
              institute patent litigation against any entity (including a
              cross-claim or counterclaim in a lawsuit) alleging that the Work
              or a Contribution incorporated within the Work constitutes direct
              or contributory patent infringement, then any patent licenses
              granted to You under this License for that Work shall terminate
              as of the date such litigation is filed.
        
           4. Redistribution. You may reproduce and distribute copies of the
              Work or Derivative Works thereof in any medium, with or without
              modifications, and in Source or Object form, provided that You
              meet the following conditions:
        
              (a) You must give any other recipients of the Work or
                  Derivative Works a copy of this License; and
        
              (b) You must cause any modified files to carry prominent notices
                  stating that You changed the files; and
        
              (c) You must retain, in the Source form of any Derivative Works
                  that You distribute, all copyright, patent, trademark, and
                  attribution notices from the Source form of the Work,
                  excluding those notices that do not pertain to any part of
                  the Derivative Works; and
        
              (d) If the Work includes a "NOTICE" text file as part of its
                  distribution, then any Derivative Works that You distribute must
                  include a readable copy of the attribution notices contained
                  within such NOTICE file, excluding those notices that do not
                  pertain to any part of the Derivative Works, in at least one
                  of the following places: within a NOTICE text file distributed
                  as part of the Derivative Works; within the Source form or
                  documentation, if provided along with the Derivative Works; or,
                  within a display generated by the Derivative Works, if and
                  wherever such third-party notices normally appear. The contents
                  of the NOTICE file are for informational purposes only and
                  do not modify the License. You may add Your own attribution
                  notices within Derivative Works that You distribute, alongside
                  or as an addendum to the NOTICE text from the Work, provided
                  that such additional attribution notices cannot be construed
                  as modifying the License.
        
              You may add Your own copyright statement to Your modifications and
              may provide additional or different license terms and conditions
              for use, reproduction, or distribution of Your modifications, or
              for any such Derivative Works as a whole, provided Your use,
              reproduction, and distribution of the Work otherwise complies with
              the conditions stated in this License.
        
           5. Submission of Contributions. Unless You explicitly state otherwise,
              any Contribution intentionally submitted for inclusion in the Work
              by You to the Licensor shall be under the terms and conditions of
              this License, without any additional terms or conditions.
              Notwithstanding the above, nothing herein shall supersede or modify
              the terms of any separate license agreement you may have executed
              with Licensor regarding such Contributions.
        
           6. Trademarks. This License does not grant permission to use the trade
              names, trademarks, service marks, or product names of the Licensor,
              except as required for reasonable and customary use in describing the
              origin of the Work and reproducing the content of the NOTICE file.
        
           7. Disclaimer of Warranty. Unless required by applicable law or
              agreed to in writing, Licensor provides the Work (and each
              Contributor provides its Contributions) on an "AS IS" BASIS,
              WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
              implied, including, without limitation, any warranties or conditions
              of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
              PARTICULAR PURPOSE. You are solely responsible for determining the
              appropriateness of using or redistributing the Work and assume any
              risks associated with Your exercise of permissions under this License.
        
           8. Limitation of Liability. In no event and under no legal theory,
              whether in tort (including negligence), contract, or otherwise,
              unless required by applicable law (such as deliberate and grossly
              negligent acts) or agreed to in writing, shall any Contributor be
              liable to You for damages, including any direct, indirect, special,
              incidental, or consequential damages of any character arising as a
              result of this License or out of the use or inability to use the
              Work (including but not limited to damages for loss of goodwill,
              work stoppage, computer failure or malfunction, or any and all
              other commercial damages or losses), even if such Contributor
              has been advised of the possibility of such damages.
        
           9. Accepting Warranty or Additional Liability. While redistributing
              the Work or Derivative Works thereof, You may choose to offer,
              and charge a fee for, acceptance of support, warranty, indemnity,
              or other liability obligations and/or rights consistent with this
              License. However, in accepting such obligations, You may act only
              on Your own behalf and on Your sole responsibility, not on behalf
              of any other Contributor, and only if You agree to indemnify,
              defend, and hold each Contributor harmless for any liability
              incurred by, or claims asserted against, such Contributor by reason
              of your accepting any such warranty or additional liability.
        
           END OF TERMS AND CONDITIONS
        
           APPENDIX: How to apply the Apache License to your work.
        
              To apply the Apache License to your work, attach the following
              boilerplate notice, with the fields enclosed by brackets "{}"
              replaced with your own identifying information. (Don't include
              the brackets!)  The text should be enclosed in the appropriate
              comment syntax for the file format. We also recommend that a
              file or class name and description of purpose be included on the
              same "printed page" as the copyright notice for easier
              identification within third-party archives.
        
           Copyright {yyyy} {name of copyright owner}
        
           Licensed under the Apache License, Version 2.0 (the "License");
           you may not use this file except in compliance with the License.
           You may obtain a copy of the License at
        
               https://www.apache.org/licenses/LICENSE-2.0
        
           Unless required by applicable law or agreed to in writing, software
           distributed under the License is distributed on an "AS IS" BASIS,
           WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
           See the License for the specific language governing permissions and
           limitations under the License.
        
Project-URL: Homepage, https://github.com/allenai/olmocr
Project-URL: Repository, https://github.com/allenai/olmocr
Project-URL: Changelog, https://github.com/allenai/olmocr/blob/main/CHANGELOG.md
Classifier: Intended Audience :: Science/Research
Classifier: Development Status :: 3 - Alpha
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.11
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: cached-path
Requires-Dist: smart_open
Requires-Dist: pypdf>=5.2.0
Requires-Dist: pypdfium2
Requires-Dist: cryptography
Requires-Dist: lingua-language-detector
Requires-Dist: Pillow
Requires-Dist: ftfy
Requires-Dist: bleach
Requires-Dist: markdown2
Requires-Dist: filelock
Requires-Dist: orjson
Requires-Dist: requests
Requires-Dist: zstandard
Requires-Dist: boto3
Requires-Dist: httpx
Requires-Dist: torch>=2.5.1
Requires-Dist: transformers==4.46.2
Requires-Dist: img2pdf
Requires-Dist: beaker-py
Provides-Extra: gpu
Requires-Dist: sgl-kernel==0.0.3.post1; extra == "gpu"
Requires-Dist: sglang[all]==0.4.2; extra == "gpu"
Provides-Extra: dev
Requires-Dist: ruff; extra == "dev"
Requires-Dist: mypy; extra == "dev"
Requires-Dist: black; extra == "dev"
Requires-Dist: isort; extra == "dev"
Requires-Dist: pytest; extra == "dev"
Requires-Dist: pytest-sphinx; extra == "dev"
Requires-Dist: pytest-cov; extra == "dev"
Requires-Dist: twine>=1.11.0; extra == "dev"
Requires-Dist: build; extra == "dev"
Requires-Dist: setuptools; extra == "dev"
Requires-Dist: wheel; extra == "dev"
Requires-Dist: Sphinx<7.1.0,>=4.3.0; extra == "dev"
Requires-Dist: furo==2023.7.26; extra == "dev"
Requires-Dist: myst-parser<2.1,>=1.0; extra == "dev"
Requires-Dist: sphinx-copybutton==0.5.2; extra == "dev"
Requires-Dist: sphinx-autobuild==2021.3.14; extra == "dev"
Requires-Dist: sphinx-autodoc-typehints==1.23.3; extra == "dev"
Requires-Dist: packaging; extra == "dev"
Requires-Dist: necessary; extra == "dev"
Requires-Dist: peft; extra == "dev"
Requires-Dist: datasets; extra == "dev"
Requires-Dist: omegaconf; extra == "dev"
Requires-Dist: spacy; extra == "dev"
Provides-Extra: bench
Requires-Dist: tinyhost; extra == "bench"
Requires-Dist: fuzzysearch; extra == "bench"
Requires-Dist: rapidfuzz; extra == "bench"
Requires-Dist: sequence_align; extra == "bench"
Requires-Dist: syntok; extra == "bench"
Requires-Dist: openai; extra == "bench"
Requires-Dist: google-genai; extra == "bench"
Requires-Dist: playwright; extra == "bench"
Requires-Dist: mistralai; extra == "bench"
Requires-Dist: lxml; extra == "bench"
Requires-Dist: flask; extra == "bench"
Provides-Extra: train
Requires-Dist: torch; extra == "train"
Requires-Dist: torchvision; extra == "train"
Requires-Dist: accelerate; extra == "train"
Requires-Dist: datasets; extra == "train"
Requires-Dist: peft; extra == "train"
Requires-Dist: wandb; extra == "train"
Requires-Dist: omegaconf; extra == "train"
Requires-Dist: s3fs; extra == "train"
Requires-Dist: necessary; extra == "train"
Requires-Dist: einops; extra == "train"
Requires-Dist: transformers>=4.45.1; extra == "train"
Provides-Extra: elo
Requires-Dist: numpy; extra == "elo"
Requires-Dist: scipy; extra == "elo"
Requires-Dist: pandas; extra == "elo"
Requires-Dist: matplotlib; extra == "elo"
Dynamic: license-file

<div align="center">
  <!-- <img src="https://github.com/allenai/OLMo/assets/8812459/774ac485-a535-4768-8f7c-db7be20f5cc3" width="300"/> -->
<img src="https://github.com/user-attachments/assets/d70c8644-3e64-4230-98c3-c52fddaeccb6" alt="olmOCR Logo" width="300"/>
<br/>
  <br>
  <h1>olmOCR</h1>
</div>
<p align="center">
  <a href="https://github.com/allenai/OLMo/blob/main/LICENSE">
    <img alt="GitHub License" src="https://img.shields.io/github/license/allenai/OLMo">
  </a>
  <a href="https://github.com/allenai/olmocr/releases">
    <img alt="GitHub release" src="https://img.shields.io/github/release/allenai/olmocr.svg">
  </a>
  <a href="https://olmocr.allenai.org/papers/olmocr.pdf">
    <img alt="Tech Report" src="https://img.shields.io/badge/Paper-olmOCR-blue">
  </a>
  <a href="https://olmocr.allenai.org">
    <img alt="Demo" src="https://img.shields.io/badge/Ai2-Demo-F0529C">
  </a>
  <a href="https://discord.gg/sZq3jTNVNG">
    <img alt="Discord" src="https://img.shields.io/badge/Discord%20-%20blue?style=flat&logo=discord&label=Ai2&color=%235B65E9">
  </a>
</p>

A toolkit for training language models to work with PDF documents in the wild.

Try the online demo: [https://olmocr.allenai.org/](https://olmocr.allenai.org/)

What is included:
 - A prompting strategy to get really good natural text parsing using ChatGPT 4o - [buildsilver.py](https://github.com/allenai/olmocr/blob/main/olmocr/data/buildsilver.py)
 - An side-by-side eval toolkit for comparing different pipeline versions - [runeval.py](https://github.com/allenai/olmocr/blob/main/olmocr/eval/runeval.py)
 - Basic filtering by language and SEO spam removal - [filter.py](https://github.com/allenai/olmocr/blob/main/olmocr/filter/filter.py)
 - Finetuning code for Qwen2-VL and Molmo-O - [train.py](https://github.com/allenai/olmocr/blob/main/olmocr/train/train.py)
 - Processing millions of PDFs through a finetuned model using Sglang - [pipeline.py](https://github.com/allenai/olmocr/blob/main/olmocr/pipeline.py)
 - Viewing [Dolma docs](https://github.com/allenai/dolma) created from PDFs - [dolmaviewer.py](https://github.com/allenai/olmocr/blob/main/olmocr/viewer/dolmaviewer.py)

### Installation

Requirements:
 - Recent NVIDIA GPU (tested on RTX 4090, L40S, A100, H100) with at least 20 GB of GPU RAM
 - 30GB of free disk space

You will need to install poppler-utils and additional fonts for rendering PDF images.

Install dependencies (Ubuntu/Debian)
```bash
sudo apt-get update
sudo apt-get install poppler-utils ttf-mscorefonts-installer msttcorefonts fonts-crosextra-caladea fonts-crosextra-carlito gsfonts lcdf-typetools
```

Set up a conda environment and install olmocr
```bash
conda create -n olmocr python=3.11
conda activate olmocr

git clone https://github.com/allenai/olmocr.git
cd olmocr

# For CPU-only operations, ex. running benchmarks
pip install -e .

# For actually converting the files with your own GPU
pip install -e .[gpu] --find-links https://flashinfer.ai/whl/cu124/torch2.4/flashinfer/
```

### Local Usage Example

For quick testing, try the [web demo](https://olmocr.allen.ai/). To run locally, a GPU is required, as inference is powered by [sglang](https://github.com/sgl-project/sglang) under the hood.
Convert a Single PDF:
```bash
python -m olmocr.pipeline ./localworkspace --pdfs tests/gnarly_pdfs/horribleocr.pdf
```

Convert an Image file:
```bash
python -m olmocr.pipeline ./localworkspace --pdfs random_page.png
```

Convert Multiple PDFs:
```bash
python -m olmocr.pipeline ./localworkspace --pdfs tests/gnarly_pdfs/*.pdf
```
Results will be stored as JSON in `./localworkspace`.

#### Viewing Results

Extracted text is stored as [Dolma](https://github.com/allenai/dolma)-style JSONL inside of the `./localworkspace/results` directory.

```bash
cat localworkspace/results/output_*.jsonl
```

View results side-by-side with the original PDFs (uses `dolmaviewer` command):

```bash
python -m olmocr.viewer.dolmaviewer localworkspace/results/output_*.jsonl
```

Now open `./dolma_previews/tests_gnarly_pdfs_horribleocr_pdf.html` in your favorite browser.

![image](https://github.com/user-attachments/assets/128922d1-63e6-4d34-84f2-d7901237da1f)


### Multi-node / Cluster Usage

If you want to convert millions of PDFs, using multiple nodes running in parallel, then olmOCR supports
reading your PDFs from AWS S3, and coordinating work using an AWS S3 output bucket.

For example, you can start this command on your first worker node, and it will set up
a simple work queue in your AWS bucket and start converting PDFs.

```bash
python -m olmocr.pipeline s3://my_s3_bucket/pdfworkspaces/exampleworkspace --pdfs s3://my_s3_bucket/jakep/gnarly_pdfs/*.pdf
```

Now on any subsequent nodes, just run this and they will start grabbing items from the same workspace queue.
```bash
python -m olmocr.pipeline s3://my_s3_bucket/pdfworkspaces/exampleworkspace
```

If you are at Ai2 and want to linearize millions of PDFs efficiently using [beaker](https://www.beaker.org), just add the `--beaker`
flag. This will prepare the workspace on your local machine, and then launch N GPU workers in the cluster to start
converting PDFs.

For example:
```bash
python -m olmocr.pipeline s3://my_s3_bucket/pdfworkspaces/exampleworkspace --pdfs s3://my_s3_bucket/jakep/gnarly_pdfs/*.pdf --beaker --beaker_gpus 4
```

### Full documentation for the pipeline

```bash
python -m olmocr.pipeline --help
usage: pipeline.py [-h] [--pdfs PDFS] [--workspace_profile WORKSPACE_PROFILE] [--pdf_profile PDF_PROFILE] [--pages_per_group PAGES_PER_GROUP]
                   [--max_page_retries MAX_PAGE_RETRIES] [--max_page_error_rate MAX_PAGE_ERROR_RATE] [--workers WORKERS] [--apply_filter] [--stats] [--model MODEL]
                   [--model_max_context MODEL_MAX_CONTEXT] [--model_chat_template MODEL_CHAT_TEMPLATE] [--target_longest_image_dim TARGET_LONGEST_IMAGE_DIM]
                   [--target_anchor_text_len TARGET_ANCHOR_TEXT_LEN] [--beaker] [--beaker_workspace BEAKER_WORKSPACE] [--beaker_cluster BEAKER_CLUSTER]
                   [--beaker_gpus BEAKER_GPUS] [--beaker_priority BEAKER_PRIORITY]
                   workspace

Manager for running millions of PDFs through a batch inference pipeline

positional arguments:
  workspace             The filesystem path where work will be stored, can be a local folder, or an s3 path if coordinating work with many workers, s3://bucket/prefix/

options:
  -h, --help            show this help message and exit
  --pdfs PDFS           Path to add pdfs stored in s3 to the workspace, can be a glob path s3://bucket/prefix/*.pdf or path to file containing list of pdf paths
  --workspace_profile WORKSPACE_PROFILE
                        S3 configuration profile for accessing the workspace
  --pdf_profile PDF_PROFILE
                        S3 configuration profile for accessing the raw pdf documents
  --pages_per_group PAGES_PER_GROUP
                        Aiming for this many pdf pages per work item group
  --max_page_retries MAX_PAGE_RETRIES
                        Max number of times we will retry rendering a page
  --max_page_error_rate MAX_PAGE_ERROR_RATE
                        Rate of allowable failed pages in a document, 1/250 by default
  --workers WORKERS     Number of workers to run at a time
  --apply_filter        Apply basic filtering to English pdfs which are not forms, and not likely seo spam
  --stats               Instead of running any job, reports some statistics about the current workspace
  --model MODEL         List of paths where you can find the model to convert this pdf. You can specify several different paths here, and the script will try to use the
                        one which is fastest to access
  --model_max_context MODEL_MAX_CONTEXT
                        Maximum context length that the model was fine tuned under
  --model_chat_template MODEL_CHAT_TEMPLATE
                        Chat template to pass to sglang server
  --target_longest_image_dim TARGET_LONGEST_IMAGE_DIM
                        Dimension on longest side to use for rendering the pdf pages
  --target_anchor_text_len TARGET_ANCHOR_TEXT_LEN
                        Maximum amount of anchor text to use (characters)
  --beaker              Submit this job to beaker instead of running locally
  --beaker_workspace BEAKER_WORKSPACE
                        Beaker workspace to submit to
  --beaker_cluster BEAKER_CLUSTER
                        Beaker clusters you want to run on
  --beaker_gpus BEAKER_GPUS
                        Number of gpu replicas to run
  --beaker_priority BEAKER_PRIORITY
                        Beaker priority level for the job
```


## Team

<!-- start team -->

**olmOCR** is developed and maintained by the AllenNLP team, backed by [the Allen Institute for Artificial Intelligence (AI2)](https://allenai.org/).
AI2 is a non-profit institute with the mission to contribute to humanity through high-impact AI research and engineering.
To learn more about who specifically contributed to this codebase, see [our contributors](https://github.com/allenai/olmocr/graphs/contributors) page.

<!-- end team -->

## License

<!-- start license -->

**olmOCR** is licensed under [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0).
A full copy of the license can be found [on GitHub](https://github.com/allenai/olmocr/blob/main/LICENSE).

<!-- end license -->

## Citing

```bibtex
@misc{olmocr,
      title={{olmOCR: Unlocking Trillions of Tokens in PDFs with Vision Language Models}},
      author={Jake Poznanski and Jon Borchardt and Jason Dunkelberger and Regan Huff and Daniel Lin and Aman Rangapur and Christopher Wilhelm and Kyle Lo and Luca Soldaini},
      year={2025},
      eprint={2502.18443},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2502.18443},
}
```