Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use devinitorg/cdp-multi-classifier-weighted with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use devinitorg/cdp-multi-classifier-weighted with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="devinitorg/cdp-multi-classifier-weighted")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("devinitorg/cdp-multi-classifier-weighted") model = AutoModelForSequenceClassification.from_pretrained("devinitorg/cdp-multi-classifier-weighted", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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# cdp-multi-classifier-weighted
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This model is a fine-tuned version of [alex-miller/ODABert](https://huggingface.co/alex-miller/ODABert)
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It achieves the following results on the evaluation set:
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- Loss: 0.8564
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- Accuracy: 0.9716
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- Precision: 0.7788
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- Recall: 0.9316
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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# cdp-multi-classifier-weighted
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This model is a fine-tuned version of [alex-miller/ODABert](https://huggingface.co/alex-miller/ODABert).
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It achieves the following results on the evaluation set:
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- Loss: 0.8564
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- Accuracy: 0.9716
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- Precision: 0.7788
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- Recall: 0.9316
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## Training procedure
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### Training hyperparameters
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