Text Classification
Transformers
Safetensors
English
deberta-v2
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use tmnam20/mdeberta-v3-base-sst2-10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tmnam20/mdeberta-v3-base-sst2-10 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tmnam20/mdeberta-v3-base-sst2-10")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tmnam20/mdeberta-v3-base-sst2-10") model = AutoModelForSequenceClassification.from_pretrained("tmnam20/mdeberta-v3-base-sst2-10", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from tmnam20/mdeberta-v3-base-sst2-10: direct link, hf CLI and curl.
- Browser
- Download file 4.73 kB
-
https://huggingface.co/tmnam20/mdeberta-v3-base-sst2-10/resolve/main/training_args.bin
- Command line
-
hf download hf://tmnam20/mdeberta-v3-base-sst2-10/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/tmnam20/mdeberta-v3-base-sst2-10/resolve/main/training_args.bin
4.73 kB
- Xet hash:
- df8b3232770c5d11d437ae51b22f66e55d88dde4255cc2dce231523f1a5967f1
- Size of remote file:
- 4.73 kB
- SHA256:
- c9222635b41d8646325db030d35ba697a483ba313cff3d0aa97cdec23bc756c3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.