Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use philschmid/distilbert-base-multilingual-cased-sentiment-2 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-2 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-2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("philschmid/distilbert-base-multilingual-cased-sentiment-2") model = AutoModelForSequenceClassification.from_pretrained("philschmid/distilbert-base-multilingual-cased-sentiment-2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e2e177e981792e8699d809867e9fcaaa3a33206a33bb4d4963870532639fd096
- Size of remote file:
- 541 MB
- SHA256:
- e0b4465e96029809fa7c5e408fe4a407909919967920b0a1f24f3c9f1c28131b
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