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 training_args.bin from Ludo33/e5_EC_v2: direct link, hf CLI and curl.
- Browser
- Download file 5.3 kB
-
https://huggingface.co/Ludo33/e5_EC_v2/resolve/main/training_args.bin
- Command line
-
hf download hf://Ludo33/e5_EC_v2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Ludo33/e5_EC_v2/resolve/main/training_args.bin
5.3 kB
- Xet hash:
- b910b9346d3e8aefada2a1fceb9fc99c6dee039fa3028fc85d67eb49f4a2e274
- Size of remote file:
- 5.3 kB
- SHA256:
- eb8b2a26342d80cbfbdad0273328665ca586506b92d9d323eabda017130deee7
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