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:
- c7df3a277727f9041eafef3d3bab1b32aab4b3fe867aa26fec5b25fdb51a0853
- Size of remote file:
- 17.1 MB
- SHA256:
- d0091a328b3441d754e481db5a390d7f3b8dabc6016869fd13ba350d23ddc4cd
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.