lambdaofgod/paperswithcode_word2vec

This is a sentence-transformers model: It maps sentences & paragraphs to a 200 dimensional dense vector space and can be used for tasks like clustering or semantic search.

Training

This model was trained on PapersWithCode dataset on abstracts and READMEs using gensim.

Usage (Sentence-Transformers)

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

model = SentenceTransformer('lambdaofgod/paperswithcode_word2vec')
embeddings = model.encode(sentences)
print(embeddings)

Full Model Architecture

SentenceTransformer(
  (0): WordEmbeddings(
    (emb_layer): Embedding(147043, 200)
  )
  (1): Pooling({'word_embedding_dimension': 200, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)

Citing & Authors