Text Classification
Transformers
Safetensors
English
deberta-v2
deberta-v3
schema-conditioned
candidate-scoring
zero-shot-classification
structured-output
text-embeddings-inference
Instructions to use mobarmg/jev-schema-scorer-deberta-v3-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mobarmg/jev-schema-scorer-deberta-v3-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mobarmg/jev-schema-scorer-deberta-v3-large")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mobarmg/jev-schema-scorer-deberta-v3-large") model = AutoModelForSequenceClassification.from_pretrained("mobarmg/jev-schema-scorer-deberta-v3-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e00c2e280f4e607de0c2d1ebdf4727cbc314f1b1d89f86cb577dd578a5574fbe
- Size of remote file:
- 1.74 GB
- SHA256:
- f17e96aec24bb0f3acad57eafa47611245b3ad77bd9c5c297f9689cc8fc04c29
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