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finbert-tone

FinBERT-Tone is a specialized text-classification model fine-tuned specifically for sentiment analysis within the financial domain. Unlike general-purpose NLP models that often struggle with the nuanced language of markets—where words like 'volatile' or 'bearish' carry specific weights—this model is optimized to categorize financial text into positive, negative, or neutral tones. For developers building algorithmic trading bots, portfolio monitors, or market sentiment dashboards, it provides a reliable way to quantify qualitative data from earnings reports, news feeds, and analyst notes. It integrates easily into standard PyTorch or Hugging Face pipelines, offering a lightweight alternative to LLMs for high-throughput sentiment labeling tasks.

yiyanghkusttext classification
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01 / MODEL CARD

Model card

FinBERT-Tone is a specialized text-classification model fine-tuned specifically for sentiment analysis within the financial domain. Unlike general-purpose NLP models that often struggle with the nuanced language of markets—where words like 'volatile' or 'bearish' carry specific weights—this model is optimized to categorize financial text into positive, negative, or neutral tones. For developers building algorithmic trading bots, portfolio monitors, or market sentiment dashboards, it provides a reliable way to quantify qualitative data from earnings reports, news feeds, and analyst notes. It integrates easily into standard PyTorch or Hugging Face pipelines, offering a lightweight alternative to LLMs for high-throughput sentiment labeling tasks.

Model typetext classification
Provideryiyanghkust
LicenseSee model card
02 / FILES & VERSIONS

Model files and versions

Model cardModel description and metadata available in this entry
Listed
Source repositoryhttps://huggingface.co/yiyanghkust/finbert-tone
View model source
Version informationUse the source repository for the latest version
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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: yiyanghkust/finbert-tone
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 yiyanghkust/finbert-tone
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 yiyanghkust/finbert-tone 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('yiyanghkust/finbert-tone')
Clone with Git

Make sure Git LFS is installed correctly.

git lfs install
git clone https://www.modelscope.cn/yiyanghkust/finbert-tone.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/yiyanghkust/finbert-tone.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.

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