Who Annotates in NLP? A Large-scale Assessment of Human Annotation Reporting between 2018 and 2025
Paper • 2606.02255 • Published
AI & ML interests
Make all hub models available for conversion to ONNX format.
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gagan3012
authored a
paper about 1 month ago
gagan3012
submitted a
paper to Daily Papers about 1 month ago
Aurelien-Morgan
posted an update 2 months ago
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1099
@retrain-pipelines v0.2.0 is out !
I'm at Station F at My booth with GOSIM Paris 2026 today & tomorrow.
Come meet me for a live in-person demo and a chat !
I'm at Station F at My booth with GOSIM Paris 2026 today & tomorrow.
Come meet me for a live in-person demo and a chat !
- 1 reply
gagan3012
authored a
paper 3 months ago
Aurelien-Morgan
posted an update 3 months ago
MaziyarPanahi
posted an update 4 months ago
Post
4445
Training mRNA Language Models Across 25 Species for $165
We built an end-to-end protein AI pipeline covering structure prediction, sequence design, and codon optimization. After comparing multiple transformer architectures for codon-level language modeling, CodonRoBERTa-large-v2 emerged as the clear winner with a perplexity of 4.10 and a Spearman CAI correlation of 0.40, significantly outperforming ModernBERT. We then scaled to 25 species, trained 4 production models in 55 GPU-hours, and built a species-conditioned system that no other open-source project offers. Complete results, architectural decisions, and runnable code below.
https://huggingface.co/blog/OpenMed/training-mrna-models-25-species
We built an end-to-end protein AI pipeline covering structure prediction, sequence design, and codon optimization. After comparing multiple transformer architectures for codon-level language modeling, CodonRoBERTa-large-v2 emerged as the clear winner with a perplexity of 4.10 and a Spearman CAI correlation of 0.40, significantly outperforming ModernBERT. We then scaled to 25 species, trained 4 production models in 55 GPU-hours, and built a species-conditioned system that no other open-source project offers. Complete results, architectural decisions, and runnable code below.
https://huggingface.co/blog/OpenMed/training-mrna-models-25-species
MaziyarPanahi
posted an update 4 months ago
Post
2393
We annotated 119K medical images with two frontier VLMs (Qwen 3.5, Kimi K2.5), cross-validated at 93% agreement, and produced 110K training records, all for under $500. Fine-tuning 3 small models (2-3B params) improved all benchmarks: best model reaches +15.0% average exact match.
Everything is open-sourced: datasets, adapters, and code.
https://huggingface.co/blog/OpenMed/synthvision
Everything is open-sourced: datasets, adapters, and code.
https://huggingface.co/blog/OpenMed/synthvision
- 2 replies
gagan3012
authored 2
papers 4 months ago
gagan3012
submitted 2
papers to Daily Papers 4 months ago
MaziyarPanahi
posted an update 4 months ago
Post
4961
DNA, mRNA, proteins, AI. I spent the last year going deep into computational biology as an ML engineer. This is Part I of what I found. 🧬
In 2024, AlphaFold won the Nobel Prize in Chemistry.
By 2026, the open-source community had built alternatives that outperform it.
That's the story I find most interesting about protein AI right now. Not just the science (which is incredible), but the speed at which open-source caught up. Multiple teams, independently, reproduced and then exceeded AlphaFold 3's accuracy with permissive licenses. The field went from prediction to generation: we're not just modeling known proteins anymore, we're designing new ones.
I spent months mapping this landscape for ML engineers. What the architectures actually are (spoiler: transformers and diffusion models), which tools to use for what, and which ones you can actually ship commercially.
New post on the Hugging Face blog: https://huggingface.co/blog/MaziyarPanahi/protein-ai-landscape
Hope you all enjoy! 🤗
In 2024, AlphaFold won the Nobel Prize in Chemistry.
By 2026, the open-source community had built alternatives that outperform it.
That's the story I find most interesting about protein AI right now. Not just the science (which is incredible), but the speed at which open-source caught up. Multiple teams, independently, reproduced and then exceeded AlphaFold 3's accuracy with permissive licenses. The field went from prediction to generation: we're not just modeling known proteins anymore, we're designing new ones.
I spent months mapping this landscape for ML engineers. What the architectures actually are (spoiler: transformers and diffusion models), which tools to use for what, and which ones you can actually ship commercially.
New post on the Hugging Face blog: https://huggingface.co/blog/MaziyarPanahi/protein-ai-landscape
Hope you all enjoy! 🤗
- 2 replies
MaziyarPanahi
authored a
paper 5 months ago
lewtun
submitted 2
papers to Daily Papers 5 months ago
MaziyarPanahi
posted an update 5 months ago
Post
2549
Announcing: OpenMed Multilingual PII Detection Models
Today I am releasing 105 open-source models for Personally Identifiable Information (PII) detection in French, German, and Italian.
All Apache 2.0 licensed. Free for commercial use. No restrictions.
Performance:
- French: 97.97% F1 (top model)
- German: 97.61% F1 (top model)
- Italian: 97.28% F1 (top model)
All top-10 models per language exceed 96% F1
Coverage:
55+ PII entity types per language
Native ID formats: NSS (French), Sozialversicherungsnummer (German), Codice Fiscale (Italian)
Language-specific address, phone, and name patterns
Training Data:
French: 49,580 samples
German: 42,250 samples
Italian: 40,944 samples
Why Multilingual?
European healthcare operates in European languages. Clinical notes, patient records, and medical documents are generated in French, German, Italian, and other languages.
Effective de-identification requires:
- Native language understanding — not translation
- Local ID format recognition — each country has unique patterns
- Cultural context awareness — names, addresses, and formats vary
- These models deliver production-ready accuracy without requiring data to leave your infrastructure or language.
HIPAA & GDPR Compliance
Built for US and European privacy regulations:
- On-premise deployment: Process data locally with zero external dependencies
- Data sovereignty: No API calls, no cloud services, no cross-border transfers
- Air-gapped capable: Deploy in fully isolated environments if required
- Regulatory-grade accuracy: Supporting Expert Determination standards
- HIPAA and GDPR compliance across languages, without compliance gaps.
Use Cases
- Hospital EHR systems: Automated patient record de-identification
- Clinical research: Multilingual dataset preparation for studies
- Insurance companies: Claims processing across
https://huggingface.co/collections/OpenMed/multilingual-pii-and-de-identification
Today I am releasing 105 open-source models for Personally Identifiable Information (PII) detection in French, German, and Italian.
All Apache 2.0 licensed. Free for commercial use. No restrictions.
Performance:
- French: 97.97% F1 (top model)
- German: 97.61% F1 (top model)
- Italian: 97.28% F1 (top model)
All top-10 models per language exceed 96% F1
Coverage:
55+ PII entity types per language
Native ID formats: NSS (French), Sozialversicherungsnummer (German), Codice Fiscale (Italian)
Language-specific address, phone, and name patterns
Training Data:
French: 49,580 samples
German: 42,250 samples
Italian: 40,944 samples
Why Multilingual?
European healthcare operates in European languages. Clinical notes, patient records, and medical documents are generated in French, German, Italian, and other languages.
Effective de-identification requires:
- Native language understanding — not translation
- Local ID format recognition — each country has unique patterns
- Cultural context awareness — names, addresses, and formats vary
- These models deliver production-ready accuracy without requiring data to leave your infrastructure or language.
HIPAA & GDPR Compliance
Built for US and European privacy regulations:
- On-premise deployment: Process data locally with zero external dependencies
- Data sovereignty: No API calls, no cloud services, no cross-border transfers
- Air-gapped capable: Deploy in fully isolated environments if required
- Regulatory-grade accuracy: Supporting Expert Determination standards
- HIPAA and GDPR compliance across languages, without compliance gaps.
Use Cases
- Hospital EHR systems: Automated patient record de-identification
- Clinical research: Multilingual dataset preparation for studies
- Insurance companies: Claims processing across
https://huggingface.co/collections/OpenMed/multilingual-pii-and-de-identification
- 1 reply
MaziyarPanahi
posted an update 5 months ago
Post
1386
From Golden Gate Bridge to Broken JSON: Why Anthropic's SAE Steering Fails for Structured Output
I ran 6 experiments trying to use Anthropic's SAE steering for JSON generation.
- Base model: 86.8% valid JSON
- Steering only: 24.4%
- Fine-tuned: 96.6%
- FSM constrained: 100%
Steering is for semantics, not syntax.
https://huggingface.co/blog/MaziyarPanahi/sae-steering-json
I ran 6 experiments trying to use Anthropic's SAE steering for JSON generation.
- Base model: 86.8% valid JSON
- Steering only: 24.4%
- Fine-tuned: 96.6%
- FSM constrained: 100%
Steering is for semantics, not syntax.
https://huggingface.co/blog/MaziyarPanahi/sae-steering-json
MaziyarPanahi
posted an update 5 months ago
Post
4142
🚨 Day 8/8: OpenMed Medical Reasoning Dataset Release - THE GRAND FINALE
Today I complete my 8-day release series with Medical-Reasoning-SFT-Mega.
The largest open medical reasoning dataset, combining 7 state-of-the-art AI models with fair distribution deduplication.
THE 7 SOURCE MODELS (Original Sample Counts):
1. Trinity-Mini: 810,284 samples
2. Qwen3-Next-80B: 604,249 samples
3. GPT-OSS-120B: 506,150 samples
4. Nemotron-Nano-30B: 444,544 samples
5. GLM-4.5-Air: 225,179 samples
6. MiniMax-M2.1: 204,773 samples
7. Baichuan-M3-235B: 124,520 samples
TOTAL BEFORE DEDUPLICATION: 2,919,699 samples
TOKEN COUNTS:
- Content tokens: 2.22 Billion
- Reasoning tokens: 1.56 Billion
- Total tokens: 3.78 Billion
- Samples with chain-of-thought: 100%
Quick Start:
All datasets Apache 2.0 licensed. Free for research and commercial use.
Thank you for following OpenMed's release series. I can't wait to see what you build. 🔥
OpenMed/Medical-Reasoning-SFT-Mega
OpenMed/Medical-Reasoning-SFT-GPT-OSS-120B-V2
OpenMed/Medical-Reasoning-SFT-Trinity-Mini
OpenMed/Medical-Reasoning-SFT-GLM_4.5_Air
OpenMed/Medical-Reasoning-SFT-MiniMax-M2.1
OpenMed/Medical-Reasoning-SFT-Qwen3-Next-80B
OpenMed/Medical-Reasoning-SFT-Nemotron-Nano-30B
OpenMed/Medical-Reasoning-SFT-Baichuan-M3-235B
https://huggingface.co/collections/OpenMed/medical-datasets
Today I complete my 8-day release series with Medical-Reasoning-SFT-Mega.
The largest open medical reasoning dataset, combining 7 state-of-the-art AI models with fair distribution deduplication.
THE 7 SOURCE MODELS (Original Sample Counts):
1. Trinity-Mini: 810,284 samples
2. Qwen3-Next-80B: 604,249 samples
3. GPT-OSS-120B: 506,150 samples
4. Nemotron-Nano-30B: 444,544 samples
5. GLM-4.5-Air: 225,179 samples
6. MiniMax-M2.1: 204,773 samples
7. Baichuan-M3-235B: 124,520 samples
TOTAL BEFORE DEDUPLICATION: 2,919,699 samples
TOKEN COUNTS:
- Content tokens: 2.22 Billion
- Reasoning tokens: 1.56 Billion
- Total tokens: 3.78 Billion
- Samples with chain-of-thought: 100%
Quick Start:
from datasets import load_dataset
ds = load_dataset("OpenMed/Medical-Reasoning-SFT-Mega")All datasets Apache 2.0 licensed. Free for research and commercial use.
Thank you for following OpenMed's release series. I can't wait to see what you build. 🔥
OpenMed/Medical-Reasoning-SFT-Mega
OpenMed/Medical-Reasoning-SFT-GPT-OSS-120B-V2
OpenMed/Medical-Reasoning-SFT-Trinity-Mini
OpenMed/Medical-Reasoning-SFT-GLM_4.5_Air
OpenMed/Medical-Reasoning-SFT-MiniMax-M2.1
OpenMed/Medical-Reasoning-SFT-Qwen3-Next-80B
OpenMed/Medical-Reasoning-SFT-Nemotron-Nano-30B
OpenMed/Medical-Reasoning-SFT-Baichuan-M3-235B
https://huggingface.co/collections/OpenMed/medical-datasets
- 6 replies
gagan3012
authored 2
papers 6 months ago