🧠
Sti Modern Workplace Class Model model by BlueAvenir
⭐ 24.3
🧠
Model

Sti Modern Workplace Class Model

by BlueAvenir hf-model--blueavenir--sti_modern_workplace_class_model
Nexus Index
24.3 Top 100%
S: Semantic 50
A: Authority 0
P: Popularity 0
R: Recency 12
Q: Quality 50
Tech Context
Vital Performance
3 DL / 30D
0.0%
Source →
Audited 24.3 FNI Score
Tiny - Params
- Context
3 Downloads
Model Information Summary
Entity Passport
Registry ID hf-model--blueavenir--sti_modern_workplace_class_model
Provider huggingface
📜

Cite this model

Academic & Research Attribution

BibTeX 
@misc{hf_model__blueavenir__sti_modern_workplace_class_model,
  author = {BlueAvenir},
  title = {Sti Modern Workplace Class Model Model},
  year = {2026},
  howpublished = {\url{https://huggingface.co/blueavenir/sti_modern_workplace_class_model}},
  note = {Accessed via Free2AITools Knowledge Fortress}
}
APA Style
BlueAvenir. (2026). Sti Modern Workplace Class Model [Model]. Free2AITools. https://huggingface.co/blueavenir/sti_modern_workplace_class_model

đŸ”ŦTechnical Deep Dive

Full Specifications [+]

Quick Commands

🤗 HF Download
huggingface-cli download blueavenir/sti_modern_workplace_class_model
đŸ“Ļ Install Lib
pip install -U transformers

⚖ī¸ Nexus Index V2.0

Methodology Index Protocol
24.3
TOP 100% SYSTEM IMPACT
Semantic (S) 50
Authority (A) 0
Popularity (P) 0
Recency (R) 12
Quality (Q) 50

đŸ’Ŧ Index Insight

FNI V2.0 for Sti Modern Workplace Class Model: Semantic (S:50), Authority (A:0), Popularity (P:0), Recency (R:12), Quality (Q:50).

Free2AITools Nexus Index

Verification Authority

HuggingFace API GitHub Metadata Arxiv Citation DB System Audit
Unbiased Data Node Refresh: VFS Live
---

🚀 What's Next?

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⚡

Deployment Guide

Understand deployment options

Technical Deep Dive

{MODEL_NAME}

This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.

Usage (Sentence-Transformers)

Using this model becomes easy when you have sentence-transformers installed:

text 
pip install -U sentence-transformers

Then you can use the model like this:

python 
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]

model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)

Usage (HuggingFace Transformers)

Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.

python 
from transformers import AutoTokenizer, AutoModel
import torch


#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)


# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']

# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')

# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')

# Compute token embeddings
with torch.no_grad():
    model_output = model(**encoded_input)

# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])

print("Sentence embeddings:")
print(sentence_embeddings)

Evaluation Results

For an automated evaluation of this model, see the Sentence Embeddings Benchmark: https://seb.sbert.net

Training

The model was trained with the parameters:

DataLoader:

torch.utils.data.dataloader.DataLoader of length 300 with parameters:

text 
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}

Loss:

sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss

Parameters of the fit()-Method:

text 
{
    "epochs": 1,
    "evaluation_steps": 0,
    "evaluator": "NoneType",
    "max_grad_norm": 1,
    "optimizer_class": "",
    "optimizer_params": {
        "lr": 2e-05
    },
    "scheduler": "WarmupLinear",
    "steps_per_epoch": 300,
    "warmup_steps": 30,
    "weight_decay": 0.01
}

Full Model Architecture

text 
SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)

Citing & Authors

⚠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
3Downloads
Hub Discussions
🤗 Data Source: Hugging Face ↗
🔄 Daily sync (03:00 UTC)

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

📊 FNI Methodology 📚 Knowledge Baseℹī¸ Verify with original source

🛡ī¸ Model Transparency Report

Technical metadata sourced from upstream repositories.

Open Metadata

🆔 Identity & Source

id
hf-model--blueavenir--sti_modern_workplace_class_model
slug
blueavenir--sti_modern_workplace_class_model
source
huggingface
author
BlueAvenir
license
tags
sentence-transformers, pytorch, xlm-roberta, feature-extraction, sentence-similarity, transformers, text-embeddings-inference, endpoints_compatible, region:us

⚙ī¸ Technical Specs

architecture
null
params billions
null
context length
null
pipeline tag
sentence-similarity

📊 Engagement & Metrics

downloads
3
stars
0
forks
0

Data indexed from public sources. Updated daily.