Feature Extraction
Transformers
Safetensors
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use Ludo33/e5_Eau_Multilabel_Topic_Sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ludo33/e5_Eau_Multilabel_Topic_Sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Ludo33/e5_Eau_Multilabel_Topic_Sentiment")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Ludo33/e5_Eau_Multilabel_Topic_Sentiment") model = AutoModel.from_pretrained("Ludo33/e5_Eau_Multilabel_Topic_Sentiment", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b3063d1b8778395d79d7f31b6e8ca15d9ff49e901135d0ebe933054e03c5cbca
- Size of remote file:
- 5.37 kB
- SHA256:
- cf02caf9d197a6a36d1a74856d17db83d2b4c085806c844215f040e9a4cdd131
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