Text Classification
Transformers
TensorBoard
Safetensors
deberta-v2
Generated from Trainer
text-embeddings-inference
Instructions to use orpe42/deberta_MP_dynamic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use orpe42/deberta_MP_dynamic with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="orpe42/deberta_MP_dynamic")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("orpe42/deberta_MP_dynamic") model = AutoModelForSequenceClassification.from_pretrained("orpe42/deberta_MP_dynamic", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download current_best/training_args.bin from orpe42/deberta_MP_dynamic: direct link, hf CLI and curl.
- Browser
- Download file 4.86 kB
-
https://huggingface.co/orpe42/deberta_MP_dynamic/resolve/main/current_best/training_args.bin
- Command line
-
hf download hf://orpe42/deberta_MP_dynamic/current_best/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/orpe42/deberta_MP_dynamic/resolve/main/current_best/training_args.bin
4.86 kB
- Xet hash:
- 025daa001a102e6c2b8ae175a8b3d4af86e8ccb027c9274902c37c352597958d
- Size of remote file:
- 4.86 kB
- SHA256:
- 4c526426209868ecde41a61a58160c30518dd2225ecfdf5ad7c7d6dd4f653350
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.