# mrm8488/deberta-v3-small-finetuned-sst2

Model from Hugging Face for text classification: mrm8488/deberta-v3-small-finetuned-sst2 · transformers

## Facts

- Type: LLM
- Task: text-classification
- Framework: transformers
- License: mit — Permissive open license: commercial use, modification, and redistribution are permitted. (https://spdx.org/licenses/MIT.html)
- Source: https://huggingface.co/mrm8488/deberta-v3-small-finetuned-sst2
- Hosting: unknown (no download location recorded)
- Files: 16 listed, none hosted here
- File counts: "listed" is every file in the source repository; only hosted files download from Hugging Bay, and the download plan lists the files it selects for one download, so its count can differ.
- Hosted here: availability unknown
- Full repo upstream: 1.14 GB
- Recommended file: 1.14 GB (3 files)
- Download: none recorded
- Per-file SHA-256: not available (no files hosted on Hugging Bay)
- Run-ready: no (Hugging Bay has not certified a run path for this model; hosted bytes are downloadable and hash-checkable only)
- Peer-assisted fallbacks: 0 verified, 0 pending review
- Downloads: 94
- Stars: 3
- Card completeness: 96%

## Description

External Hugging Face catalog record for mrm8488/deberta-v3-small-finetuned-sst2. Task: text classification. Library: transformers. Languages: en. Datasets referenced by card metadata: glue. Includes 7 model-card evaluation signals. Popularity signal at import: 98 downloads and 3 likes.

## Model Card Facts

- Declared license: mit
- Pipeline: text-classification
- Library: transformers
- Task categories: text-classification
- Languages: en
- Referenced datasets: glue
- Metrics: accuracy
- Model-index entries: deberta-v3-small
- Evaluation signals: 7 model-card eval results
- File hints: 16 upstream file samples

## Evaluation Signals

- GLUE SST2: Accuracy = 0.9403669724770642 (Text Classification)
- glue: Accuracy = 0.9403669724770642 (Text Classification)
- glue: Precision = 0.9375 (Text Classification)
- glue: Recall = 0.9459459459459459 (Text Classification)
- glue: AUC = 0.9804217184474193 (Text Classification)
- glue: F1 = 0.9417040358744394 (Text Classification)
- glue: loss = 0.21338027715682983 (Text Classification)

## Referenced Data

- Dataset: glue

## File Hints

- .gitattributes (1.2 KB)
- README.md (4.1 KB)
- added_tokens.json (18 B)
- all_results.json (407 B)
- config.json (1.0 KB)
- eval_results.json (229 B)
- model.safetensors (541 MB) LFS
- pytorch_model.bin (541 MB) LFS
- runs/events.out.tfevents.1637520886.e39b553f1d72.611.0 (8.5 KB) LFS
- runs/events.out.tfevents.1637522029.e39b553f1d72.611.2 (369 B) LFS
- special_tokens_map.json (156 B)
- spm.model (2.4 MB) LFS

## Quality Checklist

- Summary: passed
- Description: passed
- Model card metadata: passed
- License: passed
- Provenance: passed
- File inventory: passed
- Hosted bytes: passed
- Scan result: passed
- Manifest signature: pending

## Safety Notes

- External catalog metadata imported from Hugging Face public API on 2026-09-24T02:01:13.316Z.
- Open license detected as mit.
- Model-index metadata found for deberta-v3-small.
- Model card references dataset context: glue.
- Not locally hosted, mirrored, signed, or rescanned by Hugging Bay.
- Not a run kit: hosted, hash-checked bytes only; run readiness is not certified for this model yet.

## Actions

- Web: https://huggingbay.xyz/artifact/hf-model-mrm8488-deberta-v3-small-finetuned-sst2
- Files API: https://huggingbay.xyz/api/artifacts/hf-model-mrm8488-deberta-v3-small-finetuned-sst2/files?limit=100
- Metadata: https://huggingbay.xyz/api/artifacts/hf-model-mrm8488-deberta-v3-small-finetuned-sst2/metadata.json
- Distribution: https://huggingbay.xyz/api/artifacts/hf-model-mrm8488-deberta-v3-small-finetuned-sst2/distribution
- Manifest: not available until files are hosted and release checks pass.
- Request mirror: POST https://huggingbay.xyz/api/artifacts/hf-model-mrm8488-deberta-v3-small-finetuned-sst2/mirror-request
- Submit peer fallback for review: POST https://huggingbay.xyz/api/artifacts/hf-model-mrm8488-deberta-v3-small-finetuned-sst2/decentralized-fallbacks