Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use philschmid/distilbert-base-multilingual-cased-sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use philschmid/distilbert-base-multilingual-cased-sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="philschmid/distilbert-base-multilingual-cased-sentiment")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("philschmid/distilbert-base-multilingual-cased-sentiment") model = AutoModelForSequenceClassification.from_pretrained("philschmid/distilbert-base-multilingual-cased-sentiment") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 91b107abfab2aaa16a08384246633e1bd7aaa407b4f78e05bf188af40ec37de2
- Size of remote file:
- 541 MB
- SHA256:
- b8a69a3d946d158e4fcc855d671387cde2520d1862ec6e075fc941e35dc14008
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