| | --- |
| | 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 --> |
