Text Classification
Transformers
Safetensors
English
Portuguese
deberta-v2
biology
science
nlp
biomedical
filter
deberta
text-embeddings-inference
Instructions to use Madras1/DebertaBioClass with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Madras1/DebertaBioClass with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Madras1/DebertaBioClass")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Madras1/DebertaBioClass") model = AutoModelForSequenceClassification.from_pretrained("Madras1/DebertaBioClass", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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- text: "New studies regarding CRISPR technology show promise in gene editing."
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example_title: "Genetics Example 🔬"
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# DebertaBioClass 🧬🔍
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[](https://opensource.org/licenses/MIT)
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[](https://pytorch.org/)
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[](https://huggingface.co/microsoft/deberta-v3-base)
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**DebertaBioClass** is a fine-tuned DeBERTa-v3 model designed for **high-recall** filtering of biological texts. It excels at identifying biological content in large, noisy datasets, prioritizing "finding everything" even if it means capturing slightly more noise than other architectures.
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## Model Details
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example_title: "Genetics Example 🔬"
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---
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[](https://opensource.org/licenses/MIT)
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[](https://pytorch.org/)
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[](https://huggingface.co/microsoft/deberta-v3-base)
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# DebertaBioClass 🧬
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**DebertaBioClass** is a fine-tuned DeBERTa-v3 model designed for **high-recall** filtering of biological texts. It excels at identifying biological content in large, noisy datasets, prioritizing "finding everything" even if it means capturing slightly more noise than other architectures.
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## Model Details
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