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Uploaded a dataset of 115,293 illustrated pages from the Encyclopaedia Britannica, 1st edition (1768) to 14th (1929) to the Hub
huggingface.co/datasets/bigla…
huggingface.co/datasets/bigla…
Daniel van Strien
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Machine Learning Librarian @huggingface 🤗
I like big datasets and small models.
I like big datasets and small models.
1.4K Following 6.9K Followers 5.3K Posts
Latest posts 5
OCR models are getting very fast!
I OCR'd 14,483 pages of fossil fuel industry documents with LightOnOCR-3-0.8B from @LightOnIO. One RTX PRO 6000 on @huggingface Jobs with @vllm_project, about 7.7 pages/s once running. $1.67 in total, about $0.12 per 1,000 pages.
I tuned the recipe for Jobs. The video is the run in progress, with each page appearing when its text comes back.
One command to run it on your own pages: hf.co/spaces/uv-scri…
I OCR'd 14,483 pages of fossil fuel industry documents with LightOnOCR-3-0.8B from @LightOnIO. One RTX PRO 6000 on @huggingface Jobs with @vllm_project, about 7.7 pages/s once running. $1.67 in total, about $0.12 per 1,000 pages.
I tuned the recipe for Jobs. The video is the run in progress, with each page appearing when its text comes back.
One command to run it on your own pages: hf.co/spaces/uv-scri…
0:12
What does a private OCR eval on 20th-century documents find?
My agent and I ran 9 open-weight OCR models on 338 scanned government pages. A VLM judge compared the transcriptions in pairs, 10,730 times.
→ The top two are small: @LightOnIO OCR-3-0.8B and @TIIuae Falcon-OCR (270M)
→ The two 3B-class models came 4th and 7th
→ How you run a model matters: Unlimited-OCR-2 gained about 130 rating points with tiling
Built with ocr-bench on @huggingface Jobs. The pages stay private; only the scores are public: huggingface.co/datasets/davan…
v0 caveat: the judge is checked against ground truth on 19th-century books, not yet on pages like these.
IMO, more people should build private OCR evals!
Guide: github.com/davanstrien/oc…
My agent and I ran 9 open-weight OCR models on 338 scanned government pages. A VLM judge compared the transcriptions in pairs, 10,730 times.
→ The top two are small: @LightOnIO OCR-3-0.8B and @TIIuae Falcon-OCR (270M)
→ The two 3B-class models came 4th and 7th
→ How you run a model matters: Unlimited-OCR-2 gained about 130 rating points with tiling
Built with ocr-bench on @huggingface Jobs. The pages stay private; only the scores are public: huggingface.co/datasets/davan…
v0 caveat: the judge is checked against ground truth on 19th-century books, not yet on pages like these.
IMO, more people should build private OCR evals!
Guide: github.com/davanstrien/oc…
Recent text-to-speech models small enough to run on a CPU:
phonon-31m-en - 31M
Paradee-8M - 7.9M
Leokani-tts - 7M (4.5M version too)
PiVo - 1.4M (vocoder included)
Paradee runs ~62x real time on a single CPU thread.
phonon-31m-en - 31M
Paradee-8M - 7.9M
Leokani-tts - 7M (4.5M version too)
PiVo - 1.4M (vocoder included)
Paradee runs ~62x real time on a single CPU thread.
Nemotron 3.5 Transcribe from @NVIDIAAI is very fast with @vllm_project!
11.3 hours of 1971 Nixon tapes on one RTX PRO 6000 (NVFP4): under 3 minutes of transcription. Whole Job, setup included: about $0.30.
For batch jobs, I run vLLM inside the @huggingface Job and send many requests at once using my tool saturate, which tunes the concurrency as it goes. A 79.7-hour run took just under 10 minutes.
Recipe: huggingface.co/datasets/uv-sc…
11.3 hours of 1971 Nixon tapes on one RTX PRO 6000 (NVFP4): under 3 minutes of transcription. Whole Job, setup included: about $0.30.
For batch jobs, I run vLLM inside the @huggingface Job and send many requests at once using my tool saturate, which tunes the concurrency as it goes. A 79.7-hour run took just under 10 minutes.
Recipe: huggingface.co/datasets/uv-sc…
