Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use quesmed/emotion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use quesmed/emotion with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="quesmed/emotion")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("quesmed/emotion") model = AutoModelForSequenceClassification.from_pretrained("quesmed/emotion", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from quesmed/emotion: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/quesmed/emotion/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://quesmed/emotion/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/quesmed/emotion/resolve/main/pytorch_model.bin
268 MB
- Xet hash:
- c292fbe78b628c869b1201e1b8e8693001d65065297bec61ba0b8fd0b1b6dd46
- Size of remote file:
- 268 MB
- SHA256:
- 53e60fc3e91ce66c56a8c3fc8625f9ca9648e4670c39c32b62d4b01f8a9a3599
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