This is a port of the ANCE FirstP Model to sentence-transformers model: It maps sentences & paragraphs to a 768 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('sentence-transformers/msmarco-roberta-base-ance-firstp')
embeddings = model.encode(sentences)

Evaluation Results

For an automated evaluation of this model, see the Sentence Embeddings Benchmark:

Full Model Architecture

  (0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: RobertaModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
  (2): Dense({'in_features': 768, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  (3): LayerNorm(
    (norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)

Citing & Authors

Have a look at: ANCE Model

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