🧠
Chemberta 100m Mlm model by DeepChem
⭐ 40.4
🧠
Model

Chemberta 100m Mlm

by DeepChem deepchem/chemberta-100m-mlm
Free2AITools Nexus Index
40.4
S: Semantic 50

Query-time baseline · scored live at search

A: Authority 41
P: Popularity 52
R: Recency 54
Q: Quality 65
Tech Context
0.09B Params
512 Ctx
Vital Performance
32.7K DL / 30D
Source β†’

Technical Constraints

Experimental / High Latency
Low FNI signal 40.4 FNI Score
Tiny 0.09B Params
1k Context
32.7K Downloads
8G GPU ~2GB Est. VRAM
Dense ROBERTAFORMASKEDLM Architecture
Commercial MIT License
Model Information Summary
Entity Passport
Registry ID deepchem/chemberta-100m-mlm
License MIT
Provider huggingface
πŸ’Ύ

Compute Threshold

~1.4GB VRAM

Interactive
Estimate fit
β–Ό

* Static estimation for 4-Bit Quantization.

πŸ“œ

Cite this model

Academic & Research Attribution

BibTeX 
@misc{deepchem_chemberta_100m_mlm,
  author = {DeepChem},
  title = {Chemberta 100m Mlm Model},
  year = {2026},
  howpublished = {\url{https://huggingface.co/DeepChem/ChemBERTa-100M-MLM}},
  note = {Accessed via Free2AITools.}
}
APA Style
DeepChem. (2026). Chemberta 100m Mlm [Model]. Free2AITools. https://huggingface.co/DeepChem/ChemBERTa-100M-MLM

πŸ”¬Technical Deep Dive

Full Specifications [+]

Quick Commands

πŸ¦™ Ollama Run
ollama run chemberta-100m-mlm
πŸ€— HF Download
huggingface-cli download deepchem/chemberta-100m-mlm
πŸ“¦ Install Lib
pip install -U transformers

βš–οΈ Free2AITools Nexus Index V2.0

Methodology How FNI works
Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 41
Popularity (P) 52
Recency (R) 54
Quality (Q) 65

πŸ’¬ Index Insight

FNI V2.0 for Chemberta 100m Mlm: Authority (A:41), Popularity (P:52), Recency (R:54), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

Free2AITools Nexus Index

Data Sources / Provenance

HuggingFace API GitHub Metadata Arxiv Citation DB Methodology
Open data Updated: Live data
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Deployment reference

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Technical Deep Dive

⚠️ 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 β†’

πŸ“ Limitations & Considerations

  • β€’ Benchmark scores may vary based on evaluation methodology and hardware configuration.
  • β€’ VRAM requirements are estimates; actual usage depends on quantization and batch size.
  • β€’ 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
32.7KDownloads
Hub Discussions
πŸ€— Data Source: Hugging Face β†—
πŸ”„ Updated daily

Source summary: Based on Hugging Face metadata. Not a recommendation.

πŸ“Š FNI Methodology πŸ“š Knowledge Baseℹ️ Verify with original source

πŸ›‘οΈ Model Transparency Report

Technical metadata sourced from upstream repositories.

Open Metadata

πŸ†” Identity & Source

id
hf-model--deepchem--chemberta-100m-mlm
slug
deepchem--chemberta-100m-mlm
source
huggingface
author
DeepChem
license
MIT
tags
transformers, safetensors, roberta, fill-mask, cheminformatics, chemberta, masked-lm, license:mit, endpoints_compatible, region:us

βš™οΈ Technical Specs

architecture
RobertaForMaskedLM
params billions
0.09
context length
512
pipeline tag
fill-mask
vram gb
1.4
vram is estimated
true
vram formula
VRAM β‰ˆ (params * 0.75) + 0.8GB (KV) + 0.5GB (OS)

πŸ“Š Engagement & Metrics

downloads
32,707
stars
0
forks
0

Data indexed from public sources. Updated daily.