Text Classification
Transformers
PyTorch
Safetensors
Polish
bert
twitter
emotion
polish
herbert
Eval Results (legacy)
text-embeddings-inference
Instructions to use bardsai/twitter-emotion-pl-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bardsai/twitter-emotion-pl-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="bardsai/twitter-emotion-pl-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("bardsai/twitter-emotion-pl-base") model = AutoModelForSequenceClassification.from_pretrained("bardsai/twitter-emotion-pl-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from bardsai/twitter-emotion-pl-base: direct link, hf CLI and curl.
- Browser
- Download file 498 MB
-
https://huggingface.co/bardsai/twitter-emotion-pl-base/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://bardsai/twitter-emotion-pl-base/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/bardsai/twitter-emotion-pl-base/resolve/main/pytorch_model.bin
498 MB
- Xet hash:
- 8ac1c6a08fce1347cc597ad7f41789ad21f9a946725c00ca3eeb984289127f06
- Size of remote file:
- 498 MB
- SHA256:
- e091b954db5d3da1a259fa39a6e3b52b198b320020d9ebe859a6b5ba8d251d44
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.