🧠
Camembert Oml Ner model by switcode0
⭐ 39.3
🧠
Model

Camembert Oml Ner

by switcode0 hf-model--switcode0--camembert-oml-ner
Nexus Index
39.3 Top 100%
S: Semantic 50
A: Authority 0
P: Popularity 16
R: Recency 92
Q: Quality 65
Tech Context
Vital Performance
289 DL / 30D
0.0%
Source →
Audited 39.3 FNI Score
Tiny - Params
- Context
289 Downloads
Commercial MIT License
Model Information Summary
Entity Passport
Registry ID hf-model--switcode0--camembert-oml-ner
License MIT
Provider huggingface
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Cite this model

Academic & Research Attribution

BibTeX 
@misc{hf_model__switcode0__camembert_oml_ner,
  author = {switcode0},
  title = {Camembert Oml Ner Model},
  year = {2026},
  howpublished = {\url{https://huggingface.co/switcode0/camembert-oml-ner}},
  note = {Accessed via Free2AITools Knowledge Fortress}
}
APA Style
switcode0. (2026). Camembert Oml Ner [Model]. Free2AITools. https://huggingface.co/switcode0/camembert-oml-ner

đŸ”ŦTechnical Deep Dive

Full Specifications [+]

Quick Commands

🤗 HF Download
huggingface-cli download switcode0/camembert-oml-ner
đŸ“Ļ Install Lib
pip install -U transformers

⚖ī¸ Nexus Index V2.0

Methodology Index Protocol
39.3
TOP 100% SYSTEM IMPACT
Semantic (S) 50
Authority (A) 0
Popularity (P) 16
Recency (R) 92
Quality (Q) 65

đŸ’Ŧ Index Insight

FNI V2.0 for Camembert Oml Ner: Semantic (S:50), Authority (A:0), Popularity (P:16), Recency (R:92), Quality (Q:65).

Free2AITools Nexus Index

Verification Authority

HuggingFace API GitHub Metadata Arxiv Citation DB System Audit
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Deployment Guide

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Technical Deep Dive

camembert-oml-ner

This model is a fine-tuned version of camembert-base on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0554
  • F1: 0.9695
  • Precision: 0.9628
  • Recall: 0.9762

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 2e-05
  • train_batch_size: 16
  • eval_batch_size: 32
  • 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: 0.1
  • num_epochs: 20
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss F1 Precision Recall
1.0131 1.0 95 0.8927 0.0115 0.0211 0.0079
0.5139 2.0 190 0.4575 0.2831 0.3298 0.248
0.3479 3.0 285 0.3084 0.2602 0.2448 0.2778
0.2417 4.0 380 0.2274 0.6224 0.6123 0.6329
0.1958 5.0 475 0.1749 0.7096 0.6973 0.7222
0.1494 6.0 570 0.1383 0.9617 0.9515 0.9722
0.1202 7.0 665 0.1169 0.9569 0.9457 0.9683
0.1055 8.0 760 0.1009 0.9353 0.9115 0.9603
0.0978 9.0 855 0.0919 0.9558 0.9456 0.9663
0.0821 10.0 950 0.0830 0.9617 0.9532 0.9702
0.0765 11.0 1045 0.0762 0.9675 0.9609 0.9742
0.0700 12.0 1140 0.0707 0.9646 0.957 0.9722
0.0663 13.0 1235 0.0666 0.9665 0.959 0.9742
0.0609 14.0 1330 0.0630 0.9695 0.9628 0.9762
0.0667 15.0 1425 0.0607 0.9695 0.9628 0.9762
0.0573 16.0 1520 0.0584 0.9714 0.9666 0.9762
0.0550 17.0 1615 0.0570 0.9695 0.9628 0.9762
0.0577 18.0 1710 0.0563 0.9695 0.9628 0.9762
0.0528 19.0 1805 0.0557 0.9665 0.959 0.9742
0.0531 20.0 1900 0.0554 0.9695 0.9628 0.9762

Framework versions

  • Transformers 5.0.0
  • Pytorch 2.10.0+cu128
  • Datasets 4.0.0
  • Tokenizers 0.22.2

⚠ī¸ Incomplete Data

Some information about this model is not available. Use with Caution - Verify details from the original source before relying on this data.

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📝 Limitations & Considerations

  • â€ĸ Benchmark scores may vary based on evaluation methodology and hardware configuration.
  • â€ĸ VRAM requirements are estimates; actual usage depends on quantization and batch size.
  • â€ĸ FNI scores are relative rankings and may change as new models are added.
  • ⚠ License Unknown: Verify licensing terms before commercial use.

Social Proof

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AI Summary: Based on Hugging Face metadata. Not a recommendation.

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🛡ī¸ Model Transparency Report

Technical metadata sourced from upstream repositories.

Open Metadata

🆔 Identity & Source

id
hf-model--switcode0--camembert-oml-ner
slug
switcode0--camembert-oml-ner
source
huggingface
author
switcode0
license
MIT
tags
transformers, safetensors, camembert, token-classification, generated_from_trainer, base_model:almanach/camembert-base, base_model:finetune:almanach/camembert-base, license:mit, endpoints_compatible, region:us

⚙ī¸ Technical Specs

architecture
null
params billions
null
context length
null
pipeline tag
token-classification

📊 Engagement & Metrics

downloads
289
stars
0
forks
0

Data indexed from public sources. Updated daily.