lang-spot by GoldReal AI: Open-Weight Model

Model Card

By GoldReal AI, published under cc-by-sa-4.0, revision 238641a6d8e1.

Flags non-English words in mixed-language text. Linear heads on a frozen encoder: one for non-English words, one for likely names. Each word has character offsets, pnonenglish, nonenglishraw and namereview. Optional arguments: allowlist=[...] (words to keep as English) and threshold= (logit; default -3.68).

Read GoldReal AI's full model card

Flags non-English words in mixed-language text. Linear heads on a frozen encoder: one for non-English words, one for likely names.

Usage

import sys
from huggingface_hub import snapshot_download

path = snapshot_download("goldreal-ai/lang-spot")
sys.path.insert(0, path)
from span_probes import load

probe = load(path)
probe.analyze("Vamos a la playa this weekend")

Each word has character offsets, p_non_english, non_english_raw and name_review. Optional arguments: allowlist=[...] (words to keep as English) and threshold= (logit; default -3.68).

License

Heads: CC BY-SA 4.0. Encoder files: MIT.

Configuration

Architecture: ModernBertForMaskedLM
Context length (tokens): 8,192
Layers: 22
Hidden size: 384
Feed-forward size: 1,152
Attention heads: 6
Vocabulary size: 256,000
Stored precision: float32
Model type: modernbert

Identity and Version

Repository: goldreal-ai/lang-spot
Publisher: GoldReal AI
Task: Token classification
Modality: Text
Library: Not stated by the source
Parameters: Not stated by the source
Languages: en, es, hi, ne
Revision: 238641a6d8e11949bf13de10c851d72b8928a022
First published: 2026-10-12
Last updated: 2026-10-13

Files and Weights

15 files, 581.3 MB in total. The weights are 2 files totalling 563.7 MB in bin, safetensors.

Weights2 files · 563.7 MB

Configuration7 files · 33.5 KB

Tokenizer2 files · 17.6 MB

Documentation2 files · 2.0 KB

Other1 file · 93 B

Repository1 file · 1.6 KB

Every file

File	Type	Size	SHA-256
probes.safetensors	Weights	19.1 KB	457ff368b080
pytorch_model.bin	Weights	563.6 MB	be7c9ce2af94
config.json	Configuration	1.2 KB	—
probe_config.json	Configuration	879 B	—
span_probes/__init__.py	Configuration	37 B	—
span_probes/backbone.py	Configuration	15.9 KB	—
span_probes/common.py	Configuration	7.1 KB	—
span_probes/hub.py	Configuration	7.9 KB	—
special_tokens_map.json	Configuration	636 B	—
LICENSE-encoder	Documentation	1.2 KB	—
README.md	Documentation	831 B	—
requirements.txt	Other	93 B	—
.gitattributes	Repository	1.6 KB	—
tokenizer.json	Tokenizer	17.5 MB	197d4cc5406e
tokenizer_config.json	Tokenizer	46.4 KB	—

License and Download

License: cc-by-sa-4.0
Access: Open weights, no gate
Download size: 563.7 MB

Download from GoldReal AI

Released by GoldReal AI through its official repository on Hugging Face. Read the license.

Memory Requirements

Precision	Weights in memory
As published	563.7 MB

Weights only, from the published parameter count; the key-value cache and runtime add to this.

Questions About lang-spot

Can I use lang-spot commercially?

Yes. lang-spot is released under Creative Commons Attribution-ShareAlike 4.0. CC BY-SA 4.0 permits sharing and adapting, including commercially, with credit to the creator, and requires adaptations to be released under the same license.

What is lang-spot's context length?

8,192 tokens, from the maximum position embeddings in its published configuration.

Similar Models

Model · Token classification

stanford-deidentifier-base

Stanford AIMI

Stanford de-identifier was trained on a variety of radiology and biomedical documents with the goal of automatising the de-identification process while reaching satisfactory accuracy for use in production. Manuscript in-proceedings. These model weights are the recommended ones among all available deidentifier weights. This work was supported in part by the Medical Imaging and Data Resource Center (MIDRC), which is funded by the National Institute of Biomedical Imaging and Bioengineering (NIBIB) of the National Institutes of Health under contract 75N92020D00021 and through The Advanced Research Projects Agency for Health (ARPA-H)

Open weights mit 512 tokens transformers

View model

Model · Token classification

bert-portuguese-ner

Luís Filipe Cunha

This model is a fine-tuned version of neuralmind/bert-base-portuguese-cased It achieves the following results on the evaluation set: This model was fine-tunned on token classification task (NER) on Portuguese archival documents. The annotated labels are: Date, Profession, Person, Place, Organization All the training and evaluation data is available at: http://ner.epl.di.uminho.pt/ The following hyperparameters were used during training: - learningrate: 2e-05 - trainbatchsize: 16 - evalbatchsize: 16 - lrschedulertype: linear - numepochs: 4 - Transformers 4.10.0.dev0 - Pytorch 1.9.0+cu111 - Datasets 1.10.2 - Tokenizers 0.10.3

Open weights mit 512 tokens transformers

View model

Model · Token classification

privacy-filter-nemotron-GGUF

LocalAI-io

GGUF conversion of OpenMed/privacy-filter-nemotron, a fine-grained PII token-classification model — a fine-tune of openai/privacy-filter on the nvidia/Nemotron-PII dataset. It labels every token with a BIOES tag over 55 PII categories (221 classes) in a single forward pass, then decodes coherent spans with a constrained Viterbi procedure — so it can be served locally with no Python as the encoder/NER tier of a PII redactor. Where the base openai/privacy-filter covers 8 coarse categories, this fine-tune trades multilingual breadth for category depth: 55 fine-grained English categories (first/last name, government IDs, financial, healthcare, vehicle, digital, …). For the full model…

Open weights apache-2.0 gguf

View model

Model · Token classification

punctuate-all

KREDOR

This is based on Oliver Guhr's work. The difference is that it is a finetuned xlm-roberta-base instead of an xlm-roberta-large and on twelve languages instead of four. The languages are: English, German, French, Spanish, Bulgarian, Italian, Polish, Dutch, Czech, Portugese, Slovak, Slovenian. precision recall f1-score support accuracy 0.98 84425503 macro avg 0.83 0.74 0.77 84425503 weighted avg 0.98 0.98 0.98 84425503

Open weights mit 514 tokens transformers

View model

Model · Token classification

privacy-filter-multilingual-GGUF

LocalAI-io

GGUF conversion of OpenMed/privacy-filter-multilingual, a multilingual PII token-classification model (a fine-tune of openai/privacy-filter). It labels every token with a BIOES tag over 54 PII categories (217 classes) across 16 languages, so it can be served locally with no Python as the encoder/NER tier of a PII redactor. For the full model description, label space, evaluation, limitations, and citations, see the source model card — this card only covers the GGUF packaging and how to run it. This GGUF uses a custom architecture, openai-privacy-filter, that is not (yet) part of 1. privacy-filter.cpp (recommended) — a small standalone GGML engine for exactly this model family, on stock…

Open weights apache-2.0 gguf

View model

Model · Token classification

unbiased-toxic-roberta-onnx

Protect AI

This model is a conversion of unitary/unbiased-toxic-roberta to ONNX format using the Optimum library. Trained models & code to predict toxic comments on 3 Jigsaw challenges: Toxic comment classification, Unintended Bias in Toxic comments, Multilingual toxic comment classification. Built by Laura Hanu at Unitary. The huggingface models currently give different results to the detoxify library (see issue here). All challenges have a toxicity label. The toxicity labels represent the aggregate ratings of up to 10 annotators according the following schema: - Very Toxic (a very hateful, aggressive, or disrespectful comment that is very likely to make you leave a discussion or give up on sharing…

Open weights apache-2.0 514 tokens transformers

View model