Sentence Similarity
sentence-transformers
PyTorch
Safetensors
Transformers
mpnet
feature-extraction
text-embeddings-inference
Instructions to use Collab-uniba/github-issues-mpnet-st-e10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Collab-uniba/github-issues-mpnet-st-e10 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Collab-uniba/github-issues-mpnet-st-e10") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use Collab-uniba/github-issues-mpnet-st-e10 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Collab-uniba/github-issues-mpnet-st-e10") model = AutoModel.from_pretrained("Collab-uniba/github-issues-mpnet-st-e10") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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## Dataset
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For training, we used the [NLBSE22 dataset](https://nlbse2022.github.io/tools/)
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## Usage (Sentence-Transformers)
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## Training
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The model was trained
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**DataLoader**:
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## Dataset
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For training, we used the [NLBSE22 dataset](https://nlbse2022.github.io/tools/), after removing issues with empty body and duplicates.
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Similarity between title and body was used to train the sentence embedding model.
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## Usage (Sentence-Transformers)
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## Training
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The model was trained for ten epochs, using Multiple Negative Ranking Loss. We assumed that title and body of the same issue have to be similar.
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We used the following parameters:
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**DataLoader**:
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