Bios MiniLM

🚀 {MODEL_NAME}

This model from sentence-transformers maps sentences and paragraphs to a 384-dimensional dense vector space. It can be used for tasks such as clustering or semantic search.

🚀 Quick Start

Using this model becomes easy when you have sentence-transformers installed. First, install the sentence-transformers library:

pip install -U sentence-transformers

Then you can use the model like this:

from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]

model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)

✨ Features

• Maps sentences and paragraphs to a 384-dimensional dense vector space.
• Suitable for clustering and semantic search tasks.

📦 Installation

pip install -U sentence-transformers

💻 Usage Examples

Basic Usage

from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]

model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)

📚 Documentation

For an automated evaluation of this model, see the Sentence Embeddings Benchmark: https://seb.sbert.net

🔧 Technical Details

Training Details

The model was trained with the following parameters:

DataLoader: __main__.PubmedLowMemoryLoader of length 26041 with parameters:

{'batch_size': 128}

Loss: sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss with parameters:

{'scale': 20.0, 'similarity_fct': 'cos_sim'}

Parameters of the fit()-Method:

{
    "epochs": 1,
    "evaluation_steps": 2000,
    "evaluator": "__main__.PubmedTruePositiveIRetrievalEvaluator",
    "max_grad_norm": 1,
    "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
    "optimizer_params": {
        "lr": 2e-05
    },
    "scheduler": "WarmupLinear",
    "steps_per_epoch": null,
    "warmup_steps": 21,
    "weight_decay": 0.01
}

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
  (2): Normalize()
)

📄 License

No license information provided in the original document.

Citing & Authors