Text Classification
Transformers
Safetensors
deberta-v2
Generated from Trainer
text-embeddings-inference
Instructions to use mljn/mdeberta-v3-base-finetuned-climate_explicit-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mljn/mdeberta-v3-base-finetuned-climate_explicit-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mljn/mdeberta-v3-base-finetuned-climate_explicit-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mljn/mdeberta-v3-base-finetuned-climate_explicit-classification") model = AutoModelForSequenceClassification.from_pretrained("mljn/mdeberta-v3-base-finetuned-climate_explicit-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d4bcb1060be7747fa4fcfed94a249e56a53c97e0cf4b73fcae054d8fdc871951
- Size of remote file:
- 6.03 kB
- SHA256:
- 240be753d04a227c5d3f5cb0d31996c04291af9c2b748d0cebb1a3852abaee26
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.