Text Classification
Transformers
Safetensors
deberta-v2
Trained with AutoTrain
text-embeddings-inference
Instructions to use AeglosAI/aeglos-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AeglosAI/aeglos-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AeglosAI/aeglos-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("AeglosAI/aeglos-v1") model = AutoModelForSequenceClassification.from_pretrained("AeglosAI/aeglos-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cf50095e1c81adb9efd64eb7cba6927657dcc6774de2783ab7a321209d213256
- Size of remote file:
- 4.73 kB
- SHA256:
- 4e48cf00087ddedacda6b0a9a1188a5a5d85cd0226e13e702101a307f5d8d1ae
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.