distilbert base uncased finetuned sst 2 english

Providerdistilbert
Categorytext-classification
Licenseapache-2.0
Downloads2.3K
Stars1

Overview

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.

Highlights

  • Optimized for high-throughput binary sentiment classification.
  • Reduced latency and memory overhead versus standard BERT.
  • Ready-to-use weights finetuned on the SST-2 dataset.
  • Apache-2.0 license ensures flexible commercial integration.
  • Ideal for edge deployment and real-time text analysis.

Usage

Install
# Install Hugging Face transformers
pip install transformers torch
SDK Usage
# Load model with transformers
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("distilbert/distilbert-base-uncased-finetuned-sst-2-english")
tokenizer = AutoTokenizer.from_pretrained("distilbert/distilbert-base-uncased-finetuned-sst-2-english")

Hugging Face Download

We recommend downloading the model via the Hugging Face CLI or Hub SDK.

Guidance：Before downloading, install huggingface_hub with:

Guidance 
pip install -U huggingface_hub

CLI Download

Download the full repository

Download the full repository 
huggingface-cli download distilbert/distilbert-base-uncased-finetuned-sst-2-english

Download a single file to a local folder (e.g. config.json into ./dir)

Download a single file to a local folder (e.g. config.json into ./dir) 
huggingface-cli download distilbert/distilbert-base-uncased-finetuned-sst-2-english config.json --local-dir ./dir

See the official docs for more CLI options

SDK Download

SDK Download 
# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('distilbert/distilbert-base-uncased-finetuned-sst-2-english')

Git Download

Make sure git-lfs is installed first

Git Download 
git lfs install
git clone https://huggingface.co/distilbert/distilbert-base-uncased-finetuned-sst-2-english

To skip LFS large-file downloads, use:

Skip LFS 
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/distilbert/distilbert-base-uncased-finetuned-sst-2-english

Model files are hosted on the Hugging Face Hub — download directly via HF CLI / SDK / Git, not through this site.

PyTorch / Transformers Usage

Install Transformers

Install Transformers 
pip install -U transformers torch

Load the model and run inference

Load the model and run inference 
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained('distilbert/distilbert-base-uncased-finetuned-sst-2-english')
tokenizer = AutoTokenizer.from_pretrained('distilbert/distilbert-base-uncased-finetuned-sst-2-english')

Model Download

We recommend downloading the model via the ModelScope CLI or SDK.

Guidance：Before downloading, install ModelScope with:

Guidance 
pip install modelscope

CLI Download

Download the full repository

Download the full repository 
modelscope download --model distilbert/distilbert-base-uncased-finetuned-sst-2-english

Download a single file to a local folder (e.g. README.md into ./dir)

Download a single file to a local folder (e.g. README.md into ./dir) 
modelscope download --model distilbert/distilbert-base-uncased-finetuned-sst-2-english README.md --local_dir ./dir

See the docs for more CLI options

SDK Download

SDK Download 
# 模型下载
from modelscope import snapshot_download
model_dir = snapshot_download('distilbert/distilbert-base-uncased-finetuned-sst-2-english')

Git Download

Make sure git-lfs is installed first

Git Download 
git lfs install
git clone https://www.modelscope.cn/distilbert/distilbert-base-uncased-finetuned-sst-2-english.git

To skip LFS large-file downloads, use:

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

ModelScope 模型页直接下载模型文件；无需将模型文件放在本站服务器。

Notebook Quickstart

Install the ModelScope library

Install the ModelScope library 
pip install "modelscope[audio,cv,nlp,multi-modal,science]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html

Load the model and run inference

Load the model and run inference 
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks

p = pipeline('text-generation', 'distilbert/distilbert-base-uncased-finetuned-sst-2-english')

Full Documentation

来源: HuggingFace

---
language: en
license: apache-2.0
datasets:

  • sst2

  • glue

model-index:
  • name: distilbert-base-uncased-finetuned-sst-2-english

results:
- task:
type: text-classification
name: Text Classification
dataset:
name: glue
type: glue
config: sst2
split: validation
metrics:
- type: accuracy
value: 0.9105504587155964
name: Accuracy
verified: true
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- type: precision
value: 0.8978260869565218
name: Precision
verified: true
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- type: recall
value: 0.9301801801801802
name: Recall
verified: true
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- type: auc
value: 0.9716626673402374
name: AUC
verified: true
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- type: f1
value: 0.9137168141592922
name: F1
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMGU4MjNmOGYwZjZjMDQ1ZTkyZTA4YTc1MWYwOTM0NDM4ZWY1ZGVkNDY5MzNhYTQyZGFlNzIyZmUwMDg3NDU0NyIsInZlcnNpb24iOjF9.mW5ftkq50Se58M-jm6a2Pu93QeKa3MfV7xcBwvG3PSB_KNJxZWTCpfMQp-Cmx_EMlmI2siKOyd8akYjJUrzJCA
- type: loss
value: 0.39013850688934326
name: loss
verified: true
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- task:
type: text-classification
name: Text Classification
dataset:
name: sst2
type: sst2
config: default
split: train
metrics:
- type: accuracy
value: 0.9885521685548412
name: Accuracy
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiY2I3NzU3YzhmMDkxZTViY2M3OTY1NmI0ZTdmMDQxNjNjYzJiZmQxNzczM2E4YmExYTY5ODY0NDBkY2I4ZjNkOCIsInZlcnNpb24iOjF9.4Gtk3FeVc9sPWSqZIaeUXJ9oVlPzm-NmujnWpK2y5s1Vhp1l6Y1pK5_78wW0-NxSvQqV6qd5KQf_OAEpVAkQDA
- type: precision
value: 0.9881965062029833
name: Precision Macro
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZDdlZDMzY2I3MTAwYTljNmM4MGMyMzU2YjAzZDg1NDYwN2ZmM2Y5OWZhMjUyMGJiNjY1YmZiMzFhMDI2ODFhNyIsInZlcnNpb24iOjF9.cqmv6yBxu4St2mykRWrZ07tDsiSLdtLTz2hbqQ7Gm1rMzq9tdlkZ8MyJRxtME_Y8UaOG9rs68pV-gKVUs8wABw
- type: precision
value: 0.9885521685548412
name: Precision Micro
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZjFlYzAzNmE1YjljNjUwNzBjZjEzZDY0ZDQyMmY5ZWM2OTBhNzNjYjYzYTk1YWE1NjU3YTMxZDQwOTE1Y2FkNyIsInZlcnNpb24iOjF9.jnCHOkUHuAOZZ_ZMVOnetx__OVJCS6LOno4caWECAmfrUaIPnPNV9iJ6izRO3sqkHRmxYpWBb-27GJ4N3LU-BQ
- type: precision
value: 0.9885639626373408
name: Precision Weighted
verified: true
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- type: recall
value: 0.9886145346602994
name: Recall Macro
verified: true
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- type: recall
value: 0.9885521685548412
name: Recall Micro
verified: true
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- type: recall
value: 0.9885521685548412
name: Recall Weighted
verified: true
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- type: f1
value: 0.9884019815052447
name: F1 Macro
verified: true
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- type: f1
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name: F1 Micro
verified: true
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- type: f1
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name: F1 Weighted
verified: true
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- type: loss
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verified: true
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---

DistilBERT base uncased finetuned SST-2

Table of Contents

Model Details

Model Description: This model is a fine-tune checkpoint of DistilBERT-base-uncased, fine-tuned on SST-2. This model reaches an accuracy of 91.3 on the dev set (for comparison, Bert bert-base-uncased version reaches an accuracy of 92.7).
  • Developed by: Hugging Face
  • Model Type: Text Classification
  • Language(s): English
  • License: Apache-2.0
  • Parent Model: For more details about DistilBERT, we encourage users to check out this model card.
  • Resources for more information:
- Model Documentation - DistilBERT paper

How to Get Started With the Model

Example of single-label classification:
​​
```python
import torch
from transformers import DistilBertTokenizer, DistilBertForSequenceClassification

tokenizer = DistilBertTokenizer.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")
model = DistilBertForSequenceClassification.from_pretrained("distilbert-base-uncased-fi

View official source
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