🧠

Model

Xlm Roberta Large Pooled Cap Minor V5

Name: Xlm Roberta Large Pooled Cap Minor V5
Author: poltextlab

by poltextlab hf-model--poltextlab--xlm-roberta-large-pooled-cap-minor-v5

Nexus Index

44.4 Top 100%

S: Semantic 50

A: Authority 0

P: Popularity 43

R: Recency 96

Q: Quality 65

Tech Context

Vital Performance

9.0K DL / 30D

0.0%

Source →

Audited 44.4 FNI Score

Tiny - Params

- Context

9.0K Downloads

Restricted CC License

Model Information Summary
Entity Passport
Registry ID	hf-model--poltextlab--xlm-roberta-large-pooled-cap-minor-v5
License	CC-BY-4.0
Provider	huggingface

📜

Cite this model

Academic & Research Attribution

BibTeX

@misc{hf_model__poltextlab__xlm_roberta_large_pooled_cap_minor_v5,
  author = {poltextlab},
  title = {Xlm Roberta Large Pooled Cap Minor V5 Model},
  year = {2026},
  howpublished = {\url{https://huggingface.co/poltextlab/xlm-roberta-large-pooled-cap-minor-v5}},
  note = {Accessed via Free2AITools Knowledge Fortress}
}

APA Style

poltextlab. (2026). Xlm Roberta Large Pooled Cap Minor V5 [Model]. Free2AITools. https://huggingface.co/poltextlab/xlm-roberta-large-pooled-cap-minor-v5

🔬Technical Deep Dive

Full Specifications [+]

Quick Commands

🤗 HF Download

huggingface-cli download poltextlab/xlm-roberta-large-pooled-cap-minor-v5

📦 Install Lib

pip install -U transformers

⚖️ Nexus Index V2.0

Methodology Index Protocol

44.4

TOP 100% SYSTEM IMPACT

Semantic (S) 50

Authority (A) 0

Popularity (P) 43

Recency (R) 96

Quality (Q) 65

💬 Index Insight

FNI V2.0 for Xlm Roberta Large Pooled Cap Minor V5: Semantic (S:50), Authority (A:0), Popularity (P:43), Recency (R:96), Quality (Q:65).

Free2AITools Nexus Index

Verification Authority

HuggingFace API GitHub Metadata Arxiv Citation DB System Audit

Unbiased Data Node Refresh: VFS Live

---

🚀 What's Next?

📊

Find Training Datasets

Discover datasets compatible with this model

📈

Compare Benchmarks

See how this model ranks on standard tests

⚡

Technical Deep Dive

xlm-roberta-large-pooled-cap-minor-v5

This model was fine-tuned on multilingual multi-domain data labeled with the CAP Minor topics (https://www.comparativeagendas.net/pages/master-codebook).

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-minor-v5-e8checkpoint",
    task="text-classification",
    tokenizer=tokenizer,
    use_fast=False,
    token=""
)

text = ""
pipe(text)

Classification Report

Overall Performance:

Accuracy: N/A
Macro Avg: Precision: 0.68, Recall: 0.61, F1-score: 0.63
Weighted Avg: Precision: 0.81, Recall: 0.82, F1-score: 0.81

Per-Language Weighted F1-Scores:

hu: 0.88 (on 204 labels)
pt: 0.86 (on 146 labels)
en: 0.79 (on 213 labels)
it: 0.56 (on 193 labels)
es: 0.52 (on 187 labels)
fr: 0.50 (on 192 labels)
da: 0.48 (on 130 labels)

Per-Class Metrics:

Label	Precision	Recall	F1-score	Support
(100) Macroeconomics – General	0.64	0.67	0.65	2378
(101) Macroeconomics – Interest Rates	0.71	0.61	0.66	313
(103) Macroeconomics – Unemployment Rate	0.59	0.52	0.55	454
(104) Macroeconomics – Monetary Policy	0.72	0.68	0.7	645
(105) Macroeconomics – National Budget	0.75	0.81	0.78	5394
(107) Macroeconomics – Tax Code	0.74	0.79	0.77	3152
(108) Macroeconomics – Industrial Policy	0.65	0.65	0.65	871
(110) Macroeconomics – Price Control	0.62	0.48	0.54	160
(199) Macroeconomics – Other	0	0	0	15
(200) Civil Rights – General	0.59	0.59	0.59	796
(201) Civil Rights – Minority Discrimination	0.74	0.72	0.73	723
(202) Civil Rights – Gender Discrimination	0.75	0.75	0.75	554
(204) Civil Rights – Age Discrimination	0.64	0.33	0.44	54
(205) Civil Rights – Handicap Discrimination	0.74	0.66	0.69	152
(206) Civil Rights – Voting Rights	0.71	0.65	0.68	284
(207) Civil Rights – Freedom of Speech	0.72	0.67	0.69	389
(208) Civil Rights – Right to Privacy	0.71	0.62	0.66	561
(209) Civil Rights – Anti-Government	0.71	0.37	0.48	123
(299) Civil Rights – Other	0.46	0.12	0.19	51
(300) Health – General	0.61	0.6	0.6	824
(301) Health – Health Care Reform	0.49	0.46	0.47	556
(302) Health – Insurance	0.64	0.61	0.62	728
(321) Health – Drug Industry	0.65	0.62	0.64	228
(322) Health – Medical Facilities	0.77	0.69	0.73	422
(323) Health – Insurance Providers	0.77	0.61	0.68	285
(324) Health – Medical Liability	0.78	0.68	0.73	158
(325) Health – Manpower	0.66	0.69	0.67	360
(331) Health – Disease Prevention	0.79	0.78	0.78	783
(332) Health – Infants and Children	0.73	0.73	0.73	242
(333) Health – Mental Health	0.83	0.81	0.82	218
(334) Health – Long-term Care	0.74	0.71	0.72	264
(335) Health – Drug Coverage and Cost	0.78	0.78	0.78	207
(341) Health – Tobacco Abuse	0.84	0.85	0.85	232
(342) Health – Drug and Alcohol Abuse	0.75	0.64	0.69	286
(398) Health – R&D	0.72	0.75	0.73	244
(399) Health – Other	0.5	0.14	0.22	78
(400) Agriculture – General	0.67	0.67	0.67	1116
(401) Agriculture – Trade	0.52	0.5	0.51	325
(402) Agriculture – Subsidies to Farmers	0.71	0.68	0.7	776
(403) Agriculture – Food Inspection & Safety	0.69	0.7	0.69	319
(404) Agriculture – Food Marketing & Promotion	0.64	0.58	0.61	133
(405) Agriculture – Animal and Crop Disease	0.83	0.78	0.81	327
(408) Agriculture – Fisheries & Fishing	0.84	0.74	0.78	207
(498) Agriculture – R&D	0.69	0.75	0.72	111
(499) Agriculture – Other	0.63	0.44	0.52	218
(500) Labor – General	0.67	0.61	0.64	1076
(501) Labor – Worker Safety	0.74	0.71	0.73	259
(502) Labor – Employment Training	0.63	0.61	0.62	777
(503) Labor – Employee Benefits	0.68	0.63	0.65	918
(504) Labor – Labor Unions	0.69	0.66	0.67	742
(505) Labor – Fair Labor Standards	0.59	0.59	0.59	556
(506) Labor – Youth Employment	0.64	0.68	0.66	324
(529) Labor – Migrant and Seasonal	0.63	0.53	0.57	150
(599) Labor – Other	0.7	0.12	0.2	59
(600) Education – General	0.63	0.58	0.61	811
(601) Education – Higher	0.85	0.84	0.84	1836
(602) Education – Elementary & Secondary	0.77	0.8	0.78	2400
(603) Education – Underprivileged	0.51	0.46	0.48	163
(604) Education – Vocational	0.76	0.76	0.76	468
(606) Education – Special	0.73	0.76	0.74	123
(607) Education – Excellence	0.57	0.54	0.55	275
(698) Education – R&D	0.74	0.25	0.37	56
(699) Education – Other	0.72	0.24	0.36	115
(700) Environment – General	0.72	0.73	0.72	979
(701) Environment – Drinking Water	0.7	0.7	0.7	322
(703) Environment – Waste Disposal	0.84	0.82	0.83	367
(704) Environment – Hazardous Waste	0.68	0.75	0.72	208
(705) Environment – Air Pollution	0.78	0.8	0.79	645
(707) Environment – Recycling	0.81	0.89	0.85	63
(708) Environment – Indoor Hazards	0.69	0.67	0.68	49
(709) Environment – Species & Forest	0.76	0.79	0.77	476
(711) Environment – Land and Water Conservation	0.55	0.38	0.45	173
(798) Environment – R&D	0.53	0.2	0.3	44
(799) Environment – Other	0.6	0.06	0.11	51
(800) Energy – General	0.76	0.74	0.75	802
(801) Energy – Nuclear	0.79	0.72	0.75	289
(802) Energy – Electricity	0.72	0.66	0.69	328
(803) Energy – Natural Gas & Oil	0.75	0.77	0.76	646
(805) Energy – Coal	0.73	0.73	0.73	133
(806) Energy – Alternative & Renewable	0.74	0.76	0.75	270
(807) Energy – Conservation	0.65	0.69	0.67	185
(898) Energy – R&D	0.7	0.29	0.41	55
(899) Energy – Other	0	0	0	23
(900) Immigration – Immigration	0.76	0.74	0.75	1411
(999) No Policy Content	0.94	0.97	0.96	89092
(1000) Transportation – General	0.68	0.66	0.67	488
(1001) Transportation – Mass	0.64	0.6	0.62	298
(1002) Transportation – Highways	0.86	0.84	0.85	1715
(1003) Transportation – Air Travel	0.8	0.8	0.8	360
(1005) Transportation – Railroad Travel	0.79	0.8	0.79	539
(1007) Transportation – Maritime	0.79	0.81	0.8	516
(1010) Transportation – Infrastructure	0.56	0.51	0.54	220
(1098) Transportation – R&D	0	0	0	16
(1099) Transportation – Other	0.88	0.58	0.7	52
(1200) Law and Crime – General	0.59	0.56	0.58	714
(1201) Law and Crime – Agencies	0.78	0.79	0.79	1949
(1202) Law and Crime – White Collar Crime	0.56	0.58	0.57	402
(1203) Law and Crime – Illegal Drugs	0.76	0.79	0.78	384
(1204) Law and Crime – Court Administration	0.75	0.74	0.75	1601
(1205) Law and Crime – Prisons	0.79	0.75	0.77	426
(1206) Law and Crime – Juvenile Crime	0.69	0.71	0.7	173
(1207) Law and Crime – Child Abuse	0.77	0.74	0.76	273
(1208) Law and Crime – Family Issues	0.77	0.75	0.76	866
(1210) Law and Crime – Criminal & Civil Code	0.73	0.7	0.71	1480
(1211) Law and Crime – Crime Control	0.61	0.47	0.54	417
(1227) Law and Crime – Police	0.61	0.57	0.59	124
(1299) Law and Crime – Other	0.7	0.38	0.49	165
(1300) Social Welfare – General	0.66	0.62	0.64	1366
(1302) Social Welfare – Low-Income Assistance	0.63	0.63	0.63	589
(1303) Social Welfare – Elderly Assistance	0.74	0.72	0.73	971
(1304) Social Welfare – Disabled Assistance	0.65	0.68	0.66	336
(1305) Social Welfare – Volunteer Associations	0.67	0.65	0.66	422
(1308) Social Welfare – Child Care	0.72	0.77	0.74	351
(1399) Social Welfare – Other	0.5	0.02	0.05	41
(1400) Housing – General	0.69	0.68	0.68	1015
(1401) Housing – Community Development	0.47	0.42	0.44	396
(1403) Housing – Urban Development	0.55	0.54	0.55	329
(1404) Housing – Rural Housing	0	0	0	14
(1405) Housing – Rural Development	0.76	0.66	0.71	486
(1406) Housing – Low-Income Assistance	0.62	0.6	0.61	359
(1407) Housing – Veterans	0.82	0.74	0.78	42
(1408) Housing – Elderly	0.73	0.28	0.4	40
(1409) Housing – Homeless	0.68	0.74	0.71	90
(1498) Housing – R&D	0	0	0	0
(1499) Housing – Other	0.78	0.76	0.77	229
(1500) Domestic Commerce – General	0.62	0.64	0.63	1030
(1501) Domestic Commerce – Banking	0.69	0.65	0.67	745
(1502) Domestic Commerce – Securities & Commodities	0.56	0.57	0.57	282
(1504) Domestic Commerce – Consumer Finance	0.7	0.63	0.66	260
(1505) Domestic Commerce – Insurance Regulation	0.87	0.83	0.85	339
(1507) Domestic Commerce – Bankruptcy	0.66	0.65	0.66	148
(1520) Domestic Commerce – Corporate Management	0.62	0.58	0.6	636
(1521) Domestic Commerce – Small Businesses	0.75	0.73	0.74	651
(1522) Domestic Commerce – Copyrights and Patents	0.83	0.82	0.82	256
(1523) Domestic Commerce – Disaster Relief	0.79	0.75	0.77	421
(1524) Domestic Commerce – Tourism	0.73	0.77	0.75	210
(1525) Domestic Commerce – Consumer Safety	0.75	0.73	0.74	543
(1526) Domestic Commerce – Sports Regulation	0.84	0.86	0.85	794
(1598) Domestic Commerce – R&D	0	0	0	1
(1599) Domestic Commerce – Other	0.84	0.8	0.82	598
(1600) Defense – General	0.68	0.64	0.66	1165
(1602) Defense – Alliances	0.68	0.64	0.66	730
(1603) Defense – Intelligence	0.7	0.66	0.68	289
(1604) Defense – Readiness	0.72	0.73	0.72	564
(1605) Defense – Nuclear Arms	0.81	0.78	0.8	793
(1606) Defense – Military Aid	0.73	0.56	0.63	95
(1608) Defense – Personnel Issues	0.75	0.78	0.77	1284
(1610) Defense – Procurement	0.67	0.71	0.69	211
(1611) Defense – Installations & Land	0.6	0.64	0.62	160
(1612) Defense – Reserve Forces	0.6	0.59	0.59	68
(1614) Defense – Hazardous Waste	0	0	0	12
(1615) Defense – Civil	0.73	0.71	0.72	359
(1616) Defense – Civilian Personnel	0.86	0.28	0.42	65
(1617) Defense – Contractors	0.66	0.53	0.59	79
(1619) Defense – Foreign Operations	0.67	0.64	0.65	866
(1620) Defense – Claims against Military	0.72	0.67	0.69	174
(1698) Defense – R&D	0.63	0.37	0.47	46
(1699) Defense – Other	0.56	0.37	0.44	117
(1700) Technology – General	0.53	0.54	0.53	343
(1701) Technology – Space	0.82	0.86	0.84	129
(1704) Technology – Commercial Use of Space	0.88	0.44	0.58	32
(1705) Technology – Science Transfer	0.63	0.46	0.53	99
(1706) Technology – Telecommunications	0.7	0.74	0.72	345
(1707) Technology – Broadcast	0.9	0.9	0.9	1716
(1708) Technology – Weather Forecasting	0.72	0.77	0.74	62
(1709) Technology – Computers	0.72	0.73	0.73	241
(1798) Technology – R&D	0.74	0.79	0.76	638
(1799) Technology – Other	0.86	0.14	0.24	44
(1800) Foreign Trade – General	0.58	0.6	0.59	375
(1802) Foreign Trade – Trade Agreements	0.75	0.65	0.69	634
(1803) Foreign Trade – Exports	0.72	0.66	0.69	173
(1804) Foreign Trade – Private Investments	0.62	0.6	0.61	196
(1806) Foreign Trade – Competitiveness	0.63	0.62	0.62	329
(1807) Foreign Trade – Tariff & Imports	0.77	0.7	0.73	280
(1808) Foreign Trade – Exchange Rates	0.65	0.39	0.49	79
(1899) Foreign Trade – Other	0	0	0	8
(1900) International Affairs – General	0.71	0.77	0.74	2791
(1901) International Affairs – Foreign Aid	0.69	0.63	0.66	610
(1902) International Affairs – Resources Exploitation	0.62	0.32	0.42	151
(1905) International Affairs – Developing Countries	0.56	0.57	0.56	234
(1906) International Affairs – International Finance	0.64	0.63	0.63	483
(1910) International Affairs – Western Europe	0.68	0.68	0.68	1355
(1921) International Affairs – Specific Country	0.8	0.82	0.81	3664
(1925) International Affairs – Human Rights	0.7	0.7	0.7	505
(1926) International Affairs – Organizations	0.7	0.69	0.7	488
(1927) International Affairs – Terrorism	0.66	0.62	0.64	472
(1929) International Affairs – Diplomats	0.61	0.59	0.6	396
(1999) International Affairs – Other	0.65	0.18	0.28	72
(2000) Government Operations – General	0.67	0.6	0.63	1553
(2001) Government Operations – Intergovernmental Relations	0.66	0.66	0.66	2047
(2002) Government Operations – Bureaucracy	0.61	0.55	0.57	1247
(2003) Government Operations – Postal Service	0.85	0.87	0.86	269
(2004) Government Operations – Employees	0.75	0.74	0.74	1352
(2005) Government Operations – Appointments	0.76	0.78	0.77	540
(2006) Government Operations – Currency	0.71	0.6	0.65	179
(2007) Government Operations – Procurement & Contractors	0.74	0.62	0.67	577
(2008) Government Operations – Property Management	0.73	0.74	0.73	712
(2009) Government Operations – Tax Administration	0.74	0.62	0.67	238
(2010) Government Operations – Scandals	0.66	0.53	0.59	289
(2011) Government Operations – Branch Relations	0.68	0.67	0.68	1919
(2012) Government Operations – Political Campaigns	0.76	0.72	0.74	1821
(2013) Government Operations – Census & Statistics	0.89	0.84	0.87	81
(2014) Government Operations – Capital City	0.85	0.77	0.81	377
(2015) Government Operations – Claims against the Government	0.65	0.7	0.67	746
(2030) Government Operations – National Holidays	0.69	0.66	0.68	296
(2099) Government Operations – Other	0.51	0.35	0.41	323
(2100) Public Lands – General	0.61	0.32	0.42	158
(2101) Public Lands – National Parks	0.77	0.79	0.78	864
(2102) Public Lands – Indigenous Affairs	0.92	0.91	0.92	429
(2103) Public Lands – Public Lands	0.7	0.71	0.71	1137
(2104) Public Lands – Water Resources	0.77	0.75	0.76	760
(2105) Public Lands – Dependencies & Territories	0.66	0.64	0.65	307
(2199) Public Lands – Other	0.56	0.45	0.5	11
(2300) Culture – General	0.75	0.72	0.74	1363

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.

Cooperation

Model performance can be significantly improved by extending our training sets. We appreciate every submission of CAP-coded corpora (of any domain and language) at poltextlab{at}poltextlab{dot}com or by using the CAP Babel Machine.

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.

⚠️ 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

• Benchmark scores may vary based on evaluation methodology and hardware configuration.
• VRAM requirements are estimates; actual usage depends on quantization and batch size.
• 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

9.0KDownloads

Hub Discussions

🤗 Data Source: Hugging Face ↗

🔄 Daily sync (03:00 UTC)

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

📊 FNI Methodology 📚 Knowledge Baseℹ️ Verify with original source

🛡️ Model Transparency Report

Technical metadata sourced from upstream repositories.

Open Metadata

🆔 Identity & Source

id: hf-model--poltextlab--xlm-roberta-large-pooled-cap-minor-v5
slug: poltextlab--xlm-roberta-large-pooled-cap-minor-v5
source: huggingface
author: poltextlab
license: CC-BY-4.0
tags: transformers, safetensors, xlm-roberta, text-classification, pytorch, en, base_model:facebookai/xlm-roberta-large, base_model:finetune:facebookai/xlm-roberta-large, license:cc-by-4.0, model-index, endpoints_compatible, region:us, hu, pt, da, fr, it, es

⚙️ Technical Specs

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

📊 Engagement & Metrics

downloads: 9,014
stars: 0
forks: 0

Data indexed from public sources. Updated daily.

Welcome to Free2AI Tools!

Smart Search

FNI Score

You're All Set!