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
Update README.md
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README.md
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model-index:
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- name: mpnet-multilabel-sector-classifier
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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- Transformers 4.28.0
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- Pytorch 2.0.1+cu118
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- Datasets 2.12.0
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- Tokenizers 0.13.3
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model-index:
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- name: mpnet-multilabel-sector-classifier
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results: []
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datasets:
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- GIZ/sector_data
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co2_eq_emissions: 0.276132
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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- Transformers 4.28.0
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- Pytorch 2.0.1+cu118
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- Datasets 2.12.0
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- Tokenizers 0.13.3
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