Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use Hemg/EMOTION-AI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hemg/EMOTION-AI with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Hemg/EMOTION-AI")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Hemg/EMOTION-AI") model = AutoModelForSequenceClassification.from_pretrained("Hemg/EMOTION-AI", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Hemg/EMOTION-AI: direct link, hf CLI and curl.
- Browser
- Download file 5.18 kB
-
https://huggingface.co/Hemg/EMOTION-AI/resolve/main/training_args.bin
- Command line
-
hf download hf://Hemg/EMOTION-AI/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Hemg/EMOTION-AI/resolve/main/training_args.bin
5.18 kB
- Xet hash:
- 91cc99d21c6e7493f479addf9e1304e4b5da6b4914abd7254d7820d83eb893e5
- Size of remote file:
- 5.18 kB
- SHA256:
- 4549e5ab3499d60a63f6dc734f51dab5922e85540161812c6673b1a8a4e3361a
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