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/optimizer.pt from orpe42/deberta_MP_dynamic: direct link, hf CLI and curl.
- Browser
- Download file 3.48 GB
-
https://huggingface.co/orpe42/deberta_MP_dynamic/resolve/main/current_best/optimizer.pt
- Command line
-
hf download hf://orpe42/deberta_MP_dynamic/current_best/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/orpe42/deberta_MP_dynamic/resolve/main/current_best/optimizer.pt
3.48 GB
- Xet hash:
- 24893dbd980fbfb092bf3b5946576dd6914ff9c06c8001ea1477a80e24499be7
- Size of remote file:
- 3.48 GB
- SHA256:
- 098933cebcc75573f04b18546772a7d0b062fd327d7605168005256cdb9a5542
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