# How do I use Hugging Face's sentence-transformers library?

Source: https://zilliz.com/ai-faq/how-do-i-use-hugging-faces-sentencetransformers-library
Author: zilliz

Hugging Face's sentence-transformers library simplifies the process of generating dense vector representations (embeddings) for text, which are useful for semantic similarity, clustering, and search tasks. To start, install the library using `pip install sentence-transformers`. Once installed, you can load a pre-trained model like `all-MiniLM-L6-v2` (a popular lightweight option) with `from sentence_transformers import SentenceTransformer; model = SentenceTransformer('model_name')`. This model converts input text into 384-dimensional vectors by default. For example, `embeddings = model.encode(["Hello, world!", "How are you?"])` generates two vectors that numerically represent the input sentences.

The library handles most preprocessing automatically, including tokenization and padding. The `encode()` method supports lists of strings or individual sentences, and you can adjust parameters like `batch_size` for large datasets. For instance, `model.encode(sentences, batch_size=32, convert_to_tensor=True)` processes 32 sentences at a time and returns PyTorch tensors instead of NumPy arrays. You can also normalize embeddings to unit vectors with `normalize_embeddings=True`, which is useful for cosine similarity calculations. If you need to save or reload a model, use `model.save('path/')` and `SentenceTransformer('path/')` to retain custom configurations.

Practical applications include semantic search and text similarity. For example, to compare two sentences, compute their embeddings and measure cosine similarity using `from sklearn.metrics.pairwise import cosine_similarity; similarity = cosine_similarity([emb1], [emb2])[0][0]`. For clustering, use algorithms like K-means on the embeddings (`from sklearn.cluster import KMeans; kmeans.fit(embeddings)`). The library also supports multi-lingual models (e.g., `paraphrase-multilingual-MiniLM-L12-v2`) and fine-tuning custom models with your data using `model.fit(train_objectives)`. Pre-trained models are available on the [Hugging Face Hub](https://huggingface.co/models?library=sentence-transformers), allowing you to choose models optimized for specific tasks or languages.