Instructions to use ppsingh/mpnet-multilabel-sector-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ppsingh/mpnet-multilabel-sector-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ppsingh/mpnet-multilabel-sector-classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ppsingh/mpnet-multilabel-sector-classifier") model = AutoModelForSequenceClassification.from_pretrained("ppsingh/mpnet-multilabel-sector-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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## Intended uses & limitations
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## Training and evaluation data
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## Training procedure
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### Training hyperparameters
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| 0.0462 | 7.0 | 6279 | 0.2149 | 0.7447 | 0.7500 | 0.8060 | 0.8989 | 0.8989 | 0.9107 | 0.8323 |
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| 0.0336 | 8.0 | 7176 | 0.2181 | 0.7733 | 0.7780 | 0.8221 | 0.8909 | 0.8909 | 0.9031 | 0.8400 |
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### Framework versions
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- Transformers 4.28.0
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### Training hyperparameters
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| 0.0462 | 7.0 | 6279 | 0.2149 | 0.7447 | 0.7500 | 0.8060 | 0.8989 | 0.8989 | 0.9107 | 0.8323 |
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| 0.0336 | 8.0 | 7176 | 0.2181 | 0.7733 | 0.7780 | 0.8221 | 0.8909 | 0.8909 | 0.9031 | 0.8400 |
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## Environmental Impact
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*Carbon emissions were estimated using the [codecarbon](https://github.com/mlco2/codecarbon)*
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- **Hardware Type:** 16GB T4
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- **Hours used:** 3
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- **Cloud Provider:** Google Colab
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- **Carbon Emitted** : 0.276132
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### Framework versions
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- Transformers 4.28.0
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