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Model

Sentence Relation

Name: Sentence Relation
Author: Nishant2711

by Nishant2711 hf-model--nishant2711--sentence_relation

Free2AITools Nexus Index

39.0 Top 100%

S: Semantic 50

A: Authority 0

P: Popularity 2

R: Recency 97

Q: Quality 65

Tech Context

0.07B Params

512 Ctx

Vital Performance

13 DL / 30D

0.0%

Source →

Audited 39 FNI Score

Tiny 0.07B Params

1k Context

13 Downloads

8G GPU ~2GB Est. VRAM

Dense DISTILBERTFORSEQUENCECLASSIFICATION Architecture

Commercial APACHE License

Model Information Summary
Entity Passport
Registry ID	hf-model--nishant2711--sentence_relation
License	Apache-2.0
Provider	huggingface

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Compute Threshold

~1.4GB 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__nishant2711__sentence_relation,
  author = {Nishant2711},
  title = {Sentence Relation Model},
  year = {2026},
  howpublished = {\url{https://huggingface.co/Nishant2711/sentence_relation}},
  note = {Accessed via Free2AITools Knowledge Fortress}
}

APA Style

Nishant2711. (2026). Sentence Relation [Model]. Free2AITools. https://huggingface.co/Nishant2711/sentence_relation

🔬Technical Deep Dive

Full Specifications [+]

Quick Commands

🦙 Ollama Run

ollama run sentence_relation

🤗 HF Download

huggingface-cli download nishant2711/sentence_relation

📦 Install Lib

pip install -U transformers

⚖️ Free2AITools Nexus Index V2.0

Methodology Index Protocol

Semantic (S) 50

Authority (A) 0

Popularity (P) 2

Recency (R) 97

Quality (Q) 65

💬 Index Insight

FNI V2.0 for Sentence Relation: Semantic (S:50), Authority (A:0), Popularity (P:2), Recency (R:97), Quality (Q:65).

Free2AITools Nexus Index

Verification Authority

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Unbiased Data Node Refresh: VFS Live

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🚀 What's Next?

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Deployment Guide

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Technical Deep Dive

sentence_relation

This model is a fine-tuned version of distilbert-base-uncased-finetuned-sst-2-english on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 1.3359
Accuracy: 0.7647
F1: 0.8411

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 4
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 16
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 3.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
No log	1.0	230	0.6055	0.7475	0.8202
No log	2.0	460	0.5747	0.7819	0.8553
1.2550	3.0	690	1.3359	0.7647	0.8411

Framework versions

Transformers 5.0.0
Pytorch 2.10.0+cu128
Datasets 4.0.0
Tokenizers 0.22.2

⚠️ 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

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13Downloads

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--nishant2711--sentence_relation
slug: nishant2711--sentence_relation
source: huggingface
author: Nishant2711
license: Apache-2.0
tags: transformers, safetensors, distilbert, text-classification, generated_from_trainer, license:apache-2.0, text-embeddings-inference, endpoints_compatible, region:us

⚙️ Technical Specs

architecture: DistilBertForSequenceClassification
params billions: 0.07
context length: 512
pipeline tag: text-classification
vram gb: 1.4
vram is estimated: true
vram formula: VRAM ≈ (params * 0.75) + 0.8GB (KV) + 0.5GB (OS)

📊 Engagement & Metrics

downloads: 13
stars: 0
forks: 0

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