Global AI chat room · 17 online now Join now
D
MODEL Listed

distilbert-base-uncased-finetuned-sst-2-english

DistilBERT base uncased finetuned SST-2 is a lightweight, distilled version of BERT optimized for binary sentiment analysis. By reducing the model size while retaining most of the original's linguistic performance, it offers a significant speedup in inference latency and a smaller memory footprint, making it ideal for production environments with limited compute resources. Developers can integrate this model into pipelines for real-time sentiment monitoring, customer feedback sorting, or basic content moderation. Compared to full-scale BERT models, it provides a more efficient trade-off between accuracy and throughput without requiring complex quantization or pruning by the end-user.

distilberttext classification
View model source
01 / MODEL CARD

Model card

DistilBERT base uncased finetuned SST-2 is a lightweight, distilled version of BERT optimized for binary sentiment analysis. By reducing the model size while retaining most of the original's linguistic performance, it offers a significant speedup in inference latency and a smaller memory footprint, making it ideal for production environments with limited compute resources. Developers can integrate this model into pipelines for real-time sentiment monitoring, customer feedback sorting, or basic content moderation. Compared to full-scale BERT models, it provides a more efficient trade-off between accuracy and throughput without requiring complex quantization or pruning by the end-user.

Model typetext classification
Providerdistilbert
Licenseapache-2.0
02 / FILES & VERSIONS

Model files and versions

Model cardModel description and metadata available in this entry
Listed
Source repositoryhttps://huggingface.co/distilbert/distilbert-base-uncased-finetuned-sst-2-english
View model source
Version informationUse the source repository for the latest version
—
03 / DOWNLOAD

Download this model

We recommend using the ModelScope CLI or SDK. Install ModelScope first, then choose a full snapshot, single file, SDK or Git LFS workflow.

This entry points to Hugging Face. The commands use the matching ModelScope repository format; confirm that the repository exists on ModelScope before running them. Model repository: distilbert/distilbert-base-uncased-finetuned-sst-2-english
Install ModelScope

Install the CLI and SDK dependency before downloading.

pip install modelscope
Download the full model repository

Download the complete weights, configuration and model card.

modelscope download --model distilbert/distilbert-base-uncased-finetuned-sst-2-english
Download one file to a local directory

README.md is used as an example; replace it with another repository file when needed.

modelscope download --model distilbert/distilbert-base-uncased-finetuned-sst-2-english README.md --local_dir ./dir
Download with the SDK

Useful in Python projects and automation scripts.

from modelscope import snapshot_download
model_dir = snapshot_download('distilbert/distilbert-base-uncased-finetuned-sst-2-english')
Clone with Git

Make sure Git LFS is installed correctly.

git lfs install
git clone https://www.modelscope.cn/distilbert/distilbert-base-uncased-finetuned-sst-2-english.git
Clone without downloading LFS blobs

Fetch the repository structure first, then pull large files when needed.

GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/distilbert/distilbert-base-uncased-finetuned-sst-2-english.git
04 / WORKFLOW

How to use

  1. 01
    Step 1

    Read the model card and source information.

  2. 02
    Step 2

    Start with a small, non-sensitive evaluation.

  3. 03
    Step 3

    Review quality, licensing and usage limits.

  4. 04
    Step 4

    Adopt it only after validation.

05 / DISCUSSIONS

Discussions

Use this space to keep checking source information, usage experience and maintenance status.

Open source page
Email