Text Classification
Transformers
Safetensors
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use Ludo33/e5_EC_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ludo33/e5_EC_v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Ludo33/e5_EC_v2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Ludo33/e5_EC_v2") model = AutoModelForSequenceClassification.from_pretrained("Ludo33/e5_EC_v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from Ludo33/e5_EC_v2: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/Ludo33/e5_EC_v2/resolve/main/tokenizer.json
- Command line
-
hf download hf://Ludo33/e5_EC_v2/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Ludo33/e5_EC_v2/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- c7df3a277727f9041eafef3d3bab1b32aab4b3fe867aa26fec5b25fdb51a0853
- Size of remote file:
- 17.1 MB
- SHA256:
- d0091a328b3441d754e481db5a390d7f3b8dabc6016869fd13ba350d23ddc4cd
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