Text Classification
Transformers
Safetensors
deberta-v2
Generated from Trainer
text-embeddings-inference
Instructions to use mljn/mdeberta-v3-base-finetuned-temporal_focus-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mljn/mdeberta-v3-base-finetuned-temporal_focus-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mljn/mdeberta-v3-base-finetuned-temporal_focus-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mljn/mdeberta-v3-base-finetuned-temporal_focus-classification") model = AutoModelForSequenceClassification.from_pretrained("mljn/mdeberta-v3-base-finetuned-temporal_focus-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ac77b63afddbec1341d43a4caad805577c26f4e40968024f23ab462a06d204ba
- Size of remote file:
- 6.03 kB
- SHA256:
- 500df19c016ff1f1040ee7cc51b93a6fdc9528c75f23566a772b99452535f643
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