Text Classification
Transformers
Safetensors
English
Portuguese
roberta
biology
science
nlp
biomedical
filter
medical
text-embeddings-inference
Instructions to use Madras1/RobertaBioClass with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Madras1/RobertaBioClass with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Madras1/RobertaBioClass")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Madras1/RobertaBioClass") model = AutoModelForSequenceClassification.from_pretrained("Madras1/RobertaBioClass", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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example_title: Genetics Example 🔬
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pipeline_tag: text-classification
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# RobertaBioClass 🧬
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**RobertaBioClass** is a fine-tuned RoBERTa model designed to distinguish biological texts from other general topics. It was trained to filter large datasets, prioritizing high recall to ensure relevant biological content is captured.
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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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[](https://huggingface.co/tasks/text-classification)
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[](https://www.python.org/)
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[](LINK_DO_SEU_COLAB_AQUI)
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# RobertaBioClass 🧬
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**RobertaBioClass** is a fine-tuned RoBERTa model designed to distinguish biological texts from other general topics. It was trained to filter large datasets, prioritizing high recall to ensure relevant biological content is captured.
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