readme: ---
  pipeline_tag: sentence-similarity
  tags:
  - sentence-transformers
  - feature-extraction
  - sentence-similarity
  ---
  
  # lambdaofgod/paperswithcode_word2vec
  
  This is a [sentence-transformers](https://www.SBERT.net) 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.
  
  <!--- Describe your model here -->
  
  ## Usage (Sentence-Transformers)
  
  
  ```python
  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
  
  <!--- Describe where people can find more information -->