Name: bertweet base sentiment analysis
Author: finiteautomata

Overview

Bertweet-base-sentiment is a specialized transformer model fine-tuned specifically for sentiment classification within the noisy environment of social media. Unlike general-purpose BERT models, this architecture is pre-trained on a massive corpus of English tweets, making it natively proficient at handling hashtags, emojis, and the idiosyncratic slang common in short-form text. For developers, this means higher accuracy on real-world user-generated content without the need for extensive custom preprocessing. It is an ideal drop-in solution for building brand monitoring tools, customer feedback loops, or real-time social listening dashboards where detecting nuance in informal language is critical.

Highlights

Optimized for social media slang, emojis, and hashtags
High-accuracy sentiment classification for short-form text
Apache-2.0 license for flexible commercial integration
Reduced preprocessing overhead for noisy user-generated content

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("finiteautomata/bertweet-base-sentiment-analysis")
tokenizer = AutoTokenizer.from_pretrained("finiteautomata/bertweet-base-sentiment-analysis")

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

huggingface-cli download finiteautomata/bertweet-base-sentiment-analysis

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

huggingface-cli download finiteautomata/bertweet-base-sentiment-analysis config.json --local-dir ./dir

See the official docs for more CLI options

SDK Download

# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('finiteautomata/bertweet-base-sentiment-analysis')

Git Download

Make sure git-lfs is installed first

Git Download

git lfs install
git clone https://huggingface.co/finiteautomata/bertweet-base-sentiment-analysis

To skip LFS large-file downloads, use:

Skip LFS

GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/finiteautomata/bertweet-base-sentiment-analysis

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

pip install -U transformers torch

Load the model and run inference

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained('finiteautomata/bertweet-base-sentiment-analysis')
tokenizer = AutoTokenizer.from_pretrained('finiteautomata/bertweet-base-sentiment-analysis')

Full Documentation

来源: HuggingFace

---
language:
- en

tags:
- sentiment-analysis

---

Sentiment Analysis in English

bertweet-sentiment-analysis

Repository: https://github.com/finiteautomata/pysentimiento/

Model trained with SemEval 2017 corpus (around ~40k tweets). Base model is BERTweet, a RoBERTa model trained on English tweets.

Uses POS, NEG, NEU labels.

License

pysentimiento is an open-source library for non-commercial use and scientific research purposes only. Please be aware that models are trained with third-party datasets and are subject to their respective licenses.

1. TASS Dataset license
2. [SEMEval 2017 Dataset license]()

Citation

If you use pysentimiento in your work, please cite this paper

code

@misc{perez2021pysentimiento,
      title={pysentimiento: A Python Toolkit for Sentiment Analysis and SocialNLP tasks},
      author={Juan Manuel Pérez and Juan Carlos Giudici and Franco Luque},
      year={2021},
      eprint={2106.09462},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

Enjoy! 🤗