Text Classification
Transformers
Safetensors
xlm-roberta
emotion-classification
emotion
multi-label-classification
synthetic data
social-media-analysis
customer-feedback
product-reviews
brand-monitoring
multilingual
🇪🇺
region:eu
Synthetic
text-embeddings-inference
Instructions to use chipenstain/multilingual-emotion-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chipenstain/multilingual-emotion-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="chipenstain/multilingual-emotion-classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("chipenstain/multilingual-emotion-classification") model = AutoModelForSequenceClassification.from_pretrained("chipenstain/multilingual-emotion-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from chipenstain/multilingual-emotion-classification: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/chipenstain/multilingual-emotion-classification/resolve/main/tokenizer.json
- Command line
-
hf download hf://chipenstain/multilingual-emotion-classification/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/chipenstain/multilingual-emotion-classification/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- 516f2fc365650acf148b86e45907397a0c35af523df9e2ea3f88e25c76c1f057
- Size of remote file:
- 17.1 MB
- SHA256:
- acbd420e2269cdc1ef45332d3d5c418be4aef6b8cb5a0b7ccae0893485307153
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