Instructions to use Lutech-AI/I-SPIn with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Lutech-AI/I-SPIn with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import ISPIn model = ISPIn.from_pretrained("Lutech-AI/I-SPIn", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Francesca Del Bonifro commited on
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Update README.md
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README.md
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**I**talian-**S**entence **P**air **In**ference, AKA **I-SPIn**.<br>
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This is a fine-tuned version of the model [paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2).<br>
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Its main task is to perform the [Natural Language Inference (NLI)](https://nlp.stanford.edu/projects/snli/) task in the Italian language.<br>
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The prediction label has two possible values: 1 means the model predicts <em>entailment</em>,
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## How it was trained
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1. Train [paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) on the NLI task;
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2. Apply Knowledge Distillation on the output of (1.) with IT-EN translation dataset to retain NLI knowledge and improve Italian language comprehension.
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**I**talian-**S**entence **P**air **In**ference, AKA **I-SPIn**.<br>
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This is a fine-tuned version of the model [paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2).<br>
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Its main task is to perform the [Natural Language Inference (NLI)](https://nlp.stanford.edu/projects/snli/) task in the Italian language.<br>
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The prediction label has two possible values: 1 means the model predicts <em>entailment</em>, 0 represents the <em>neutral</em> case
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while -1 corresponds to <em>contradiction</em>.
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## How it was trained
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1. Train [paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) on the NLI task;
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2. Apply Knowledge Distillation on the output of (1.) with IT-EN translation dataset to retain NLI knowledge and improve Italian language comprehension.
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