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 best_model/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/best_model/training_args.bin
- Command line
-
hf download hf://orpe42/deberta_MP_dynamic/best_model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/orpe42/deberta_MP_dynamic/resolve/main/best_model/training_args.bin
4.86 kB
- Xet hash:
- 4ae691c7eb400e32553590f4ac3d46a85277f4076ad3d53b5e4b64484831285c
- Size of remote file:
- 4.86 kB
- SHA256:
- d820a0794a222a7c8caa98f4c7a6bc9c8021dce822df7b310a9d7ebee075b857
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