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--- |
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pipeline_tag: sentence-similarity |
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tags: |
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- sentence-transformers |
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- feature-extraction |
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- sentence-similarity |
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--- |
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# lambdaofgod/paperswithcode_word2vec |
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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. |
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## Training |
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This model was trained on PapersWithCode dataset on abstracts and READMEs using gensim. |
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<!--- Describe your model here --> |
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## Usage (Sentence-Transformers) |
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```python |
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from sentence_transformers import SentenceTransformer |
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sentences = ["This is an example sentence", "Each sentence is converted"] |
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model = SentenceTransformer('lambdaofgod/paperswithcode_word2vec') |
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embeddings = model.encode(sentences) |
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print(embeddings) |
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``` |
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## Full Model Architecture |
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``` |
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SentenceTransformer( |
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(0): WordEmbeddings( |
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(emb_layer): Embedding(147043, 200) |
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) |
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(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}) |
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) |
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``` |
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## Citing & Authors |
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<!--- Describe where people can find more information --> |
