Constructionembeddingbert

🚀 ConstructionEmbeddingBERT

This is a model that maps sentences & paragraphs to a 1536 - dimensional dense vector space and can be used for tasks like clustering or semantic search.

🚀 Quick Start

This is a sentence-transformers model. It can map sentences and paragraphs to a 1536 - dimensional dense vector space, which is useful for tasks such as clustering or semantic search.

✨ Features

• It belongs to the sentence-transformers category, suitable for feature extraction and sentence similarity tasks.
• Maps sentences and paragraphs to a 1536 - dimensional dense vector space.

📦 Installation

Using this model becomes easy when you have sentence-transformers installed:

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

Evaluation Results

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

Training

The model was trained with the following parameters:

DataLoader: torch.utils.data.dataloader.DataLoader of length 125 with parameters:

{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}

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

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

Parameters of the fit()-Method:

{
    "epochs": 1,
    "evaluation_steps": 0,
    "evaluator": "NoneType",
    "max_grad_norm": 1,
    "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
    "optimizer_params": {
        "lr": 2e-05
    },
    "scheduler": "WarmupLinear",
    "steps_per_epoch": null,
    "warmup_steps": 12,
    "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': 1536, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
  (2): Dense({'in_features': 768, 'out_features': 1536, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
)

🔧 Technical Details

The model is based on the sentence-transformers framework. It uses a BertModel for text encoding, followed by a pooling layer to aggregate word embeddings into sentence embeddings, and finally a dense layer to adjust the embedding dimension to 1536. The training process uses MultipleNegativesRankingLoss as the loss function and AdamW as the optimizer.

📄 License

No license information is provided in the original document.

Citing & Authors