🧠
Mbart Finetuned Eng Ind 35 model by hopkins
⭐ 24.5
🧠
Model

Mbart Finetuned Eng Ind 35

by hopkins hf-model--hopkins--mbart-finetuned-eng-ind-35
Nexus Index
24.5 Top 100%
S: Semantic 50
A: Authority 0
P: Popularity 0
R: Recency 13
Q: Quality 50
Tech Context
Vital Performance
3 DL / 30D
0.0%
Source →
Audited 24.5 FNI Score
Tiny - Params
- Context
3 Downloads
Model Information Summary
Entity Passport
Registry ID hf-model--hopkins--mbart-finetuned-eng-ind-35
Provider huggingface
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Cite this model

Academic & Research Attribution

BibTeX 
@misc{hf_model__hopkins__mbart_finetuned_eng_ind_35,
  author = {hopkins},
  title = {Mbart Finetuned Eng Ind 35 Model},
  year = {2026},
  howpublished = {\url{https://huggingface.co/hopkins/mbart-finetuned-eng-ind-35}},
  note = {Accessed via Free2AITools Knowledge Fortress}
}
APA Style
hopkins. (2026). Mbart Finetuned Eng Ind 35 [Model]. Free2AITools. https://huggingface.co/hopkins/mbart-finetuned-eng-ind-35

đŸ”ŦTechnical Deep Dive

Full Specifications [+]

Quick Commands

🤗 HF Download
huggingface-cli download hopkins/mbart-finetuned-eng-ind-35
đŸ“Ļ Install Lib
pip install -U transformers

⚖ī¸ Nexus Index V2.0

Methodology Index Protocol
24.5
TOP 100% SYSTEM IMPACT
Semantic (S) 50
Authority (A) 0
Popularity (P) 0
Recency (R) 13
Quality (Q) 50

đŸ’Ŧ Index Insight

FNI V2.0 for Mbart Finetuned Eng Ind 35: Semantic (S:50), Authority (A:0), Popularity (P:0), Recency (R:13), Quality (Q:50).

Free2AITools Nexus Index

Verification Authority

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

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Technical Deep Dive

mbart-finetuned-eng-ind-35

This model is a fine-tuned version of facebook/mbart-large-50-many-to-many-mmt on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 1.7681
  • Bleu: 21.8412

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: 2e-05
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 3
  • mixed_precision_training: Native AMP

Training results

Framework versions

  • Transformers 4.26.1
  • Pytorch 2.0.1+cu117
  • Datasets 2.12.0
  • Tokenizers 0.13.3

⚠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

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

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

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🛡ī¸ Model Transparency Report

Technical metadata sourced from upstream repositories.

Open Metadata

🆔 Identity & Source

id
hf-model--hopkins--mbart-finetuned-eng-ind-35
slug
hopkins--mbart-finetuned-eng-ind-35
source
huggingface
author
hopkins
license
tags
transformers, pytorch, tensorboard, mbart, text2text-generation, translation, generated_from_trainer, endpoints_compatible, region:us

⚙ī¸ Technical Specs

architecture
null
params billions
null
context length
null
pipeline tag
translation

📊 Engagement & Metrics

downloads
3
stars
0
forks
0

Data indexed from public sources. Updated daily.