Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use Hildah-N/distilbert-emotion-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hildah-N/distilbert-emotion-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Hildah-N/distilbert-emotion-classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Hildah-N/distilbert-emotion-classifier") model = AutoModelForSequenceClassification.from_pretrained("Hildah-N/distilbert-emotion-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Hildah-N/distilbert-emotion-classifier: direct link, hf CLI and curl.
- Browser
- Download file 5.2 kB
-
https://huggingface.co/Hildah-N/distilbert-emotion-classifier/resolve/main/training_args.bin
- Command line
-
hf download hf://Hildah-N/distilbert-emotion-classifier/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Hildah-N/distilbert-emotion-classifier/resolve/main/training_args.bin
5.2 kB
- Xet hash:
- 649a09840b4d81a350e737e2eec767fbe0b947d7cd15732e8d71881562f77c54
- Size of remote file:
- 5.2 kB
- SHA256:
- 4a8ab22c2d643188aa92266c3cfe790a2378f9a26fb762999f8574cd1164517b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.