Text Classification
Transformers
PyTorch
Safetensors
Polish
distilbert
twitter
emotion
distiluse
Eval Results (legacy)
text-embeddings-inference
Instructions to use bardsai/twitter-emotion-pl-fast with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bardsai/twitter-emotion-pl-fast with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="bardsai/twitter-emotion-pl-fast")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("bardsai/twitter-emotion-pl-fast") model = AutoModelForSequenceClassification.from_pretrained("bardsai/twitter-emotion-pl-fast", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from bardsai/twitter-emotion-pl-fast: direct link, hf CLI and curl.
- Browser
- Download file 541 MB
-
https://huggingface.co/bardsai/twitter-emotion-pl-fast/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://bardsai/twitter-emotion-pl-fast/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/bardsai/twitter-emotion-pl-fast/resolve/main/pytorch_model.bin
541 MB
- Xet hash:
- 8916c2836c4aee9605feae85c50ee1abdbe73a611aa183cc18f30f58e484537c
- Size of remote file:
- 541 MB
- SHA256:
- 7e89e666006d22b690f4471fa8b34a6a9c7bd1628fd2cdb53c454fc0e901a672
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