🧠
Model

Xlm Roberta Large Pooled Cap V3

by poltextlab hf-model--poltextlab--xlm-roberta-large-pooled-cap-v3
Nexus Index
41.7 Top 100%
S: Semantic 50
A: Authority 0
P: Popularity 31
R: Recency 96
Q: Quality 50
Tech Context
Vital Performance
1.8K DL / 30D
0.0%
Audited 41.7 FNI Score
Tiny - Params
- Context
1.8K Downloads
Restricted CC License
Model Information Summary
Entity Passport
Registry ID hf-model--poltextlab--xlm-roberta-large-pooled-cap-v3
License CC-BY-4.0
Provider huggingface
📜

Cite this model

Academic & Research Attribution

BibTeX
@misc{hf_model__poltextlab__xlm_roberta_large_pooled_cap_v3,
  author = {poltextlab},
  title = {Xlm Roberta Large Pooled Cap V3 Model},
  year = {2026},
  howpublished = {\url{https://huggingface.co/poltextlab/xlm-roberta-large-pooled-cap-v3}},
  note = {Accessed via Free2AITools Knowledge Fortress}
}
APA Style
poltextlab. (2026). Xlm Roberta Large Pooled Cap V3 [Model]. Free2AITools. https://huggingface.co/poltextlab/xlm-roberta-large-pooled-cap-v3

đŸ”ŦTechnical Deep Dive

Full Specifications [+]

Quick Commands

🤗 HF Download
huggingface-cli download poltextlab/xlm-roberta-large-pooled-cap-v3
đŸ“Ļ Install Lib
pip install -U transformers

âš–ī¸ Nexus Index V2.0

41.7
TOP 100% SYSTEM IMPACT
Semantic (S) 50
Authority (A) 0
Popularity (P) 31
Recency (R) 96
Quality (Q) 50

đŸ’Ŧ Index Insight

FNI V2.0 for Xlm Roberta Large Pooled Cap V3: Semantic (S:50), Authority (A:0), Popularity (P:31), Recency (R:96), Quality (Q:50).

Free2AITools Nexus Index

Verification Authority

Unbiased Data Node Refresh: VFS Live
---

🚀 What's Next?

Technical Deep Dive

xlm-roberta-large-pooled-cap-v3

Model description

An xlm-roberta-large benchmark model finetuned on training data containing texts labelled with major topic codes from the Comparative Agendas Project.

Classification Report

Overall Performance:

  • Accuracy: 82.1%
  • Macro Avg: Precision: 0.80, Recall: 0.80, F1-score: 0.80
  • Weighted Avg: Precision: 0.82, Recall: 0.82, F1-score: 0.82

Per-Class Metrics:

Label Precision Recall F1-score Support
(1) Macroeconomics 0.74 0.78 0.76 34,802
(2) Civil Rights 0.74 0.64 0.68 14,687
(3) Health 0.85 0.88 0.86 27,158
(4) Agriculture 0.82 0.85 0.83 15,708
(5) Labor 0.77 0.74 0.76 18,803
(6) Education 0.85 0.90 0.87 23,547
(7) Environment 0.82 0.81 0.81 14,474
(8) Energy 0.87 0.80 0.83 11,549
(9) Immigration 0.78 0.77 0.77 8,310
(10) Transportation 0.88 0.81 0.84 22,611
(12) Law and Crime 0.80 0.83 0.81 36,014
(13) Social Welfare 0.80 0.77 0.78 17,322
(14) Housing 0.77 0.76 0.77 11,784
(15) Banking, Finance, and Domestic Commerce 0.79 0.77 0.78 25,184
(16) Defense 0.83 0.80 0.81 24,929
(17) Technology 0.82 0.81 0.82 12,578
(18) Foreign Trade 0.79 0.77 0.78 10,066
(19) International Affairs 0.76 0.78 0.77 33,759
(20) Government Operations 0.79 0.79 0.79 57,340
(21) Public Lands 0.79 0.83 0.81 18,803
(23) Culture 0.72 0.81 0.76 11,569
(999) No Policy Content 0.94 0.94 0.94 87,862

Gated access

Due to the gated access, you must pass the token parameter when loading the model. In earlier versions of the Transformers package, you may need to use the use_auth_token parameter instead.

How to use the model

python
from transformers import AutoTokenizer, pipeline

tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-large")
pipe = pipeline(
    model="poltextlab/xlm-roberta-large-pooled-cap-v3",
    task="text-classification",
    tokenizer=tokenizer,
    use_fast=False,
    token=""
)

text = "We will place an immediate 6-month halt on the finance driven closure of beds and wards, and set up an independent audit of needs and facilities."
pipe(text)

Inference platform

This model is used by the CAP Babel Machine, an open-source and free natural language processing tool, designed to simplify and speed up projects for comparative research.

Debugging and issues

This architecture uses the sentencepiece tokenizer. In order to run the model before transformers==4.27 you need to install it manually.

If you encounter a RuntimeError when loading the model using the from_pretrained() method, adding ignore_mismatched_sizes=True should solve the issue.

âš 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.

View Original Source →

📝 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

HuggingFace Hub
1.8KDownloads
🔄 Daily sync (03:00 UTC)

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

📊 FNI Methodology 📚 Knowledge Baseâ„šī¸ Verify with original source

đŸ›Ąī¸ Model Transparency Report

Technical metadata sourced from upstream repositories.

Open Metadata

🆔 Identity & Source

id
hf-model--poltextlab--xlm-roberta-large-pooled-cap-v3
slug
poltextlab--xlm-roberta-large-pooled-cap-v3
source
huggingface
author
poltextlab
license
CC-BY-4.0
tags
transformers, pytorch, xlm-roberta, text-classification, es, pt, pl, it, hu, de, fr, en, nl, da, license:cc-by-4.0, model-index, endpoints_compatible, region:us

âš™ī¸ Technical Specs

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

📊 Engagement & Metrics

downloads
1,797
stars
0
forks
0

Data indexed from public sources. Updated daily.