CORD 19 Title Abstracts 1 More Epoch

🚀 CShorten/CORD-19-Title-Abstracts-1-more-epoch

This is a sentence-transformers model that maps sentences and paragraphs to a 384-dimensional dense vector space, useful for tasks such as clustering or semantic search.

🚀 Quick Start

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

✨ Features

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

📦 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('CShorten/CORD-19-Title-Abstracts-1-more-epoch')
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 parameters:

DataLoader: torch.utils.data.dataloader.DataLoader of length 3750 with parameters:

{'batch_size': 32, '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": 10000,
    "weight_decay": 0.01
}

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 256, '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()
)

🔧 Technical Details

The model is based on the sentence-transformers framework. It uses a Transformer model (BertModel) for encoding sentences, followed by a pooling layer to get a fixed-length vector representation, and finally a normalization layer. The training process involves a MultipleNegativesRankingLoss function with specific parameters for optimization.

📄 License

No license information provided in the original document.

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