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
Update README.md
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README.md
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- Madras1/BioClass80k
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base_model: microsoft/deberta-v3-base
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widget:
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---
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[](https://opensource.org/licenses/MIT)
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[](https://pytorch.org/)
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- Madras1/BioClass80k
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base_model: microsoft/deberta-v3-base
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widget:
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- text: The mitochondria is the powerhouse of the cell and generates ATP.
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example_title: Biology Example 🧬
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- text: The stock market crashed today due to high inflation rates.
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example_title: Finance Example 💰
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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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pipeline_tag: text-classification
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---
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[](https://opensource.org/licenses/MIT)
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[](https://pytorch.org/)
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