Text Classification
Transformers
Safetensors
deberta-v2
Generated from Trainer
text-embeddings-inference
Instructions to use fassabilf/indonli-deberta-v3-base_10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fassabilf/indonli-deberta-v3-base_10 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="fassabilf/indonli-deberta-v3-base_10")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("fassabilf/indonli-deberta-v3-base_10") model = AutoModelForSequenceClassification.from_pretrained("fassabilf/indonli-deberta-v3-base_10", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7fd836d6f81b985236a7d2519cfaa576b2a65548f8448601539ad462081252b1
- Size of remote file:
- 5.3 kB
- SHA256:
- 14813290f49456a046e6aaa9439d0e476c913bda4ba185bd50c5156d3e403a84
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.