README.md

metadata

pipeline_tag: sentence-similarity
tags:
  - sentence-transformers
  - feature-extraction
  - sentence-similarity

{MODEL_NAME}

This is a sentence-transformers model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search.

Usage (Sentence-Transformers)

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

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)

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 111704 with parameters:

{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}

Loss:

models.Framework.Loss

Parameters of the fit()-Method:

{
    "epochs": 3,
    "evaluation_steps": 0,
    "evaluator": "NoneType",
    "max_grad_norm": 1,
    "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
    "optimizer_params": {
        "eps": 1e-06,
        "lr": 2e-05
    },
    "scheduler": "warmupconstant",
    "steps_per_epoch": null,
    "warmup_steps": 5000,
    "weight_decay": 0.01
}

Full Model Architecture

Framework(
  (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: DistilBertModel 
  (1): Pooling({'word_embedding_dimension': 768, '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': 512, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity', 'proj_token_embs': True})
  (3): Projector({'in_features': 512, 'out_features': 768, 'bias': True, 'noise_std': 0.01, 'dropout': 0.1, 'noise_prob': 0, 'student_emb_keyname': 'token_embeddings', 'teacher_emb_keyname': 'source_embedding'})
  (4): Decoder({'max_seq_length': 128, 'do_lower_case': False, 'attend_to': ['student'], 'teacher_model_name': 'clip-ViT-B-32'}) with Transformer model: BertLMHeadModel 
)

Citing & Authors