Instructions to use BillyLin/text-emotion-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BillyLin/text-emotion-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="BillyLin/text-emotion-classification")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("BillyLin/text-emotion-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from BillyLin/text-emotion-classification: direct link, hf CLI and curl.
- Browser
- Download file 2.31 kB
-
https://huggingface.co/BillyLin/text-emotion-classification/resolve/main/README.md
- Command line
-
hf download hf://BillyLin/text-emotion-classification/README.md
-
curl -L -o README.md https://huggingface.co/BillyLin/text-emotion-classification/resolve/main/README.md
2.31 kB
| license: apache-2.0 | |
| tags: | |
| - text-classification | |
| - emotion | |
| language: | |
| - zh | |
| - en | |
| library_name: transformers | |
| # text-emotion-classification | |
| A text emotion recognition application that can be quickly deployed and used locally. You can perform interactive inference simply by running `main.py`. | |
| [中文版](./README_zh.md) | |
| ## Features | |
| - **Local Inference**: Loads the `sentiment_roberta` model directory within the repository for text emotion classification. | |
| - **Label Mapping**: Reads `id -> Chinese Emotion Name` mapping from `text-emotion.yaml`. | |
| - **Interactive CLI**: Enter text in the command line to output the emotion category and confidence level. | |
| ## Directory Structure (Key Files) | |
| - `main.py`: Entry script (run directly). | |
| - `sentiment_roberta/`: Exported Transformers model directory (contains `config.json`, `model.safetensors`, tokenizer, etc.). | |
| - `text-emotion.yaml`: Label mapping file. | |
| - `release-note.md`: Release notes (used by GitHub Actions as the release body). | |
| ## Environment Requirements | |
| - **Python 3.10** (Recommended, matches the author's environment; 3.9+ is theoretically compatible but not fully verified). | |
| - **Dependency Management**: Conda environment (recommended) or venv. | |
| - **PyTorch**: | |
| - **CPU Inference**: Install the CPU version of `torch`. | |
| - **GPU Inference**: Requires an NVIDIA GPU + corresponding CUDA version (the author's environment uses `torch==2.10.0+cu128` / `torchvision==0.25.0+cu128` built with CUDA 12.8). | |
| The author's conda environment export file is provided: `environment.yml`. | |
| ## Installation | |
| ### Using Conda Environment File (Recommended) | |
| ```bash | |
| conda env create -f environment.yml | |
| conda activate text-emotion-classification | |
| ``` | |
| ## Usage | |
| ```bash | |
| python main.py | |
| ``` | |
| Follow the prompts to enter text: | |
| - **Enter any text**: Outputs emotion prediction and confidence. | |
| - **Empty input (Enter)**: Exits the program. | |
| ## FAQ | |
| - **Cannot find model directory `sentiment_roberta`** | |
| - Ensure `sentiment_roberta/` exists in the root directory and contains files like `config.json` and `model.safetensors`. | |
| - **Inference Device** | |
| - The program automatically selects `cuda` if available; otherwise, it defaults to `cpu`. | |
| ## License | |
| See [Apache 2.0 License](./LICENSE). | |