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Model

Hit Minilm L12 Wordnetnoun

Name: Hit Minilm L12 Wordnetnoun
Author: Hierarchy Transformers

by Hierarchy Transformers hf-model--hierarchy-transformers--hit-minilm-l12-wordnetnoun

Free2AITools Nexus Index

37.9 Top 100%

S: Semantic 50

A: Authority 0

P: Popularity 44

R: Recency 48

Q: Quality 65

Tech Context

0.03B Params

512 Ctx

Vital Performance

10.9K DL / 30D

0.0%

Source →

Audited 37.9 FNI Score

Tiny 0.03B Params

1k Context

10.9K Downloads

8G GPU ~2GB Est. VRAM

Dense BERTMODEL Architecture

Commercial APACHE License

Model Information Summary
Entity Passport
Registry ID	hf-model--hierarchy-transformers--hit-minilm-l12-wordnetnoun
License	Apache-2.0
Provider	huggingface

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Compute Threshold

~1.3GB VRAM

Interactive

Analyze Hardware

Hardware Compatibility Test

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* Static estimation for 4-Bit Quantization.

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Cite this model

Academic & Research Attribution

BibTeX

@misc{hf_model__hierarchy_transformers__hit_minilm_l12_wordnetnoun,
  author = {Hierarchy Transformers},
  title = {Hit Minilm L12 Wordnetnoun Model},
  year = {2026},
  howpublished = {\url{https://huggingface.co/Hierarchy-Transformers/HiT-MiniLM-L12-WordNetNoun}},
  note = {Accessed via Free2AITools Knowledge Fortress}
}

APA Style

Hierarchy Transformers. (2026). Hit Minilm L12 Wordnetnoun [Model]. Free2AITools. https://huggingface.co/Hierarchy-Transformers/HiT-MiniLM-L12-WordNetNoun

🔬Technical Deep Dive

Full Specifications [+]

Quick Commands

🦙 Ollama Run

ollama run hit-minilm-l12-wordnetnoun

🤗 HF Download

huggingface-cli download hierarchy-transformers/hit-minilm-l12-wordnetnoun

📦 Install Lib

pip install -U transformers

⚖️ Free2AITools Nexus Index V2.0

Methodology Index Protocol

Semantic (S) 50

Authority (A) 0

Popularity (P) 44

Recency (R) 48

Quality (Q) 65

💬 Index Insight

FNI V2.0 for Hit Minilm L12 Wordnetnoun: Semantic (S:50), Authority (A:0), Popularity (P:44), Recency (R:48), Quality (Q:65).

Free2AITools Nexus Index

Verification Authority

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Technical Deep Dive

Hierarchy-Transformers/HiT-MiniLM-L12-WordNetNoun

A Hierarchy Transformer Encoder (HiT) model that explicitly encodes entities according to their hierarchical relationships.

Model Description

HiT-MiniLM-L12-WordNet is a HiT model trained on WordNet's subsumption (hypernym) hierarchy of noun entities.

Developed by: Yuan He, Zhangdie Yuan, Jiaoyan Chen, and Ian Horrocks
Model type: Hierarchy Transformer Encoder (HiT)
License: Apache license 2.0
Hierarchy: WordNet's subsumption (hypernym) hierarchy of noun entities.
Training Dataset: Hierarchy-Transformers/WordNetNoun
Pre-trained model: sentence-transformers/all-MiniLM-L12-v2
Training Objectives: Jointly optimised on Hyperbolic Clustering and Hyperbolic Centripetal losses (see definitions in the paper)

Model Versions

Version	Model Revision	Note
v1.0 (Random Negatives)	`main` or `v1-random-negatives`	The variant trained on random negatives, as detailed in the paper.
v1.0 (Hard Negatives)	`v1-hard-negatives`	The variant trained on hard negatives, as detailed in the paper.

Model Sources

Repository: https://github.com/KRR-Oxford/HierarchyTransformers
Paper: Language Models as Hierarchy Encoders

Usage

HiT models are used to encode entities (presented as texts) and predict their hierarhical relationships in hyperbolic space.

Get Started

Install hierarchy_transformers (check our repository) through pip or GitHub.

Use the code below to get started with the model.

python

from hierarchy_transformers import HierarchyTransformer

# load the model
model = HierarchyTransformer.from_pretrained('Hierarchy-Transformers/HiT-MiniLM-L12-WordNetNoun')

# entity names to be encoded.
entity_names = ["computer", "personal computer", "fruit", "berry"]

# get the entity embeddings
entity_embeddings = model.encode(entity_names)

Default Probing for Subsumption Prediction

Use the entity embeddings to predict the subsumption relationships between them.

python

# suppose we want to compare "personal computer" and "computer", "berry" and "fruit"
child_entity_embeddings = model.encode(["personal computer", "berry"], convert_to_tensor=True)
parent_entity_embeddings = model.encode(["computer", "fruit"], convert_to_tensor=True)

# compute the hyperbolic distances and norms of entity embeddings
dists = model.manifold.dist(child_entity_embeddings, parent_entity_embeddings)
child_norms = model.manifold.dist0(child_entity_embeddings)
parent_norms = model.manifold.dist0(parent_entity_embeddings)

# use the empirical function for subsumption prediction proposed in the paper
# `centri_score_weight` and the overall threshold are determined on the validation set
subsumption_scores = - (dists + centri_score_weight * (parent_norms - child_norms))

Train Your Own Models

Use the example scripts in our repository to reproduce existing models and train/evaluate your own models.

Full Model Architecture

text

HierarchyTransformer(
  (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False})
)

Citation

Yuan He, Zhangdie Yuan, Jiaoyan Chen, Ian Horrocks. Language Models as Hierarchy Encoders. Advances in Neural Information Processing Systems 37 (NeurIPS 2024).

text

@article{he2024language,
  title={Language models as hierarchy encoders},
  author={He, Yuan and Yuan, Moy and Chen, Jiaoyan and Horrocks, Ian},
  journal={Advances in Neural Information Processing Systems},
  volume={37},
  pages={14690--14711},
  year={2024}
}

Model Card Contact

For any queries or feedback, please contact Yuan He (yuan.he(at)cs.ox.ac.uk).

⚠️ 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

• 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

10.9KDownloads

Hub Discussions

🤗 Data Source: Hugging Face ↗

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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--hierarchy-transformers--hit-minilm-l12-wordnetnoun
slug: hierarchy-transformers--hit-minilm-l12-wordnetnoun
source: huggingface
author: Hierarchy Transformers
license: Apache-2.0
tags: hierarchy-transformers, safetensors, bert, feature-extraction, hierarchy-encoding, subsumption-relationships, transformers, en, arxiv:2401.11374, license:apache-2.0, text-embeddings-inference, endpoints_compatible, region:us

⚙️ Technical Specs

architecture: BertModel
params billions: 0.03
context length: 512
pipeline tag: feature-extraction
vram gb: 1.3
vram is estimated: true
vram formula: VRAM ≈ (params * 0.75) + 0.8GB (KV) + 0.5GB (OS)

📊 Engagement & Metrics

downloads: 10,892
stars: 0
forks: 0

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