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:
- 1f9cc34ec81b892ddc41b5e9bff71881bebd4a645ef9feb425450ff0134785e7
- Size of remote file:
- 2.86 kB
- SHA256:
- 918d24daf65f724982ca591281cefffa3542c19bfe6eea026be5ca2222715e83
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