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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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- transformers |
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- transformers |
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- transformers |
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- transformers |
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- transformers |
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- transformers |
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- transformers |
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- transformers |
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- transformers |
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- transformers |
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- transformers |
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- transformers |
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- transformers |
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- transformers |
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- transformers |
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- transformers |
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- transformers |
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- transformers |
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--- |
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# sentence-transformers/clip-ViT-B-32 |
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This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a None dimensional dense vector space and can be used for tasks like clustering or semantic search. |
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## Usage (Sentence-Transformers) |
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Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: |
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``` |
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pip install -U sentence-transformers |
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``` |
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Then you can use the model like this: |
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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('sentence-transformers/clip-ViT-B-32') |
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embeddings = model.encode(sentences) |
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print(embeddings) |
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``` |
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## Evaluation Results |
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For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/clip-ViT-B-32) |
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## Full Model Architecture |
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``` |
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SentenceTransformer( |
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(0): CLIPModel( |
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(model): CLIP( |
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(visual): VisualTransformer( |
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(conv1): Conv2d(3, 768, kernel_size=(32, 32), stride=(32, 32), bias=False) |
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(ln_pre): LayerNorm((768,), eps=1e-05, elementwise_affine=True) |
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(transformer): Transformer( |
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(resblocks): Sequential( |
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(0): ResidualAttentionBlock( |
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(attn): MultiheadAttention( |
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(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) |
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) |
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(ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) |
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(mlp): Sequential( |
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(c_fc): Linear(in_features=768, out_features=3072, bias=True) |
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(gelu): QuickGELU() |
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(c_proj): Linear(in_features=3072, out_features=768, bias=True) |
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) |
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(ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) |
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) |
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(1): ResidualAttentionBlock( |
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(attn): MultiheadAttention( |
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(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) |
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) |
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(ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) |
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(mlp): Sequential( |
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(c_fc): Linear(in_features=768, out_features=3072, bias=True) |
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(gelu): QuickGELU() |
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(c_proj): Linear(in_features=3072, out_features=768, bias=True) |
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) |
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(ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) |
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) |
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(2): ResidualAttentionBlock( |
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(attn): MultiheadAttention( |
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(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) |
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) |
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(ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) |
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(mlp): Sequential( |
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(c_fc): Linear(in_features=768, out_features=3072, bias=True) |
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(gelu): QuickGELU() |
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(c_proj): Linear(in_features=3072, out_features=768, bias=True) |
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) |
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(ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) |
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) |
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(3): ResidualAttentionBlock( |
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(attn): MultiheadAttention( |
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(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) |
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) |
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(ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) |
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(mlp): Sequential( |
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(c_fc): Linear(in_features=768, out_features=3072, bias=True) |
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(gelu): QuickGELU() |
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(c_proj): Linear(in_features=3072, out_features=768, bias=True) |
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) |
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(ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) |
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) |
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(4): ResidualAttentionBlock( |
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(attn): MultiheadAttention( |
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(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) |
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) |
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(ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) |
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(mlp): Sequential( |
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(c_fc): Linear(in_features=768, out_features=3072, bias=True) |
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(gelu): QuickGELU() |
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(c_proj): Linear(in_features=3072, out_features=768, bias=True) |
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) |
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(ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) |
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) |
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(5): ResidualAttentionBlock( |
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(attn): MultiheadAttention( |
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(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) |
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) |
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(ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) |
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(mlp): Sequential( |
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(c_fc): Linear(in_features=768, out_features=3072, bias=True) |
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(gelu): QuickGELU() |
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(c_proj): Linear(in_features=3072, out_features=768, bias=True) |
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) |
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(ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) |
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) |
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(6): ResidualAttentionBlock( |
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(attn): MultiheadAttention( |
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(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) |
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) |
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(ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) |
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(mlp): Sequential( |
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(c_fc): Linear(in_features=768, out_features=3072, bias=True) |
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(gelu): QuickGELU() |
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(c_proj): Linear(in_features=3072, out_features=768, bias=True) |
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) |
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(ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) |
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) |
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(7): ResidualAttentionBlock( |
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(attn): MultiheadAttention( |
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(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) |
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) |
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(ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) |
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(mlp): Sequential( |
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(c_fc): Linear(in_features=768, out_features=3072, bias=True) |
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(gelu): QuickGELU() |
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(c_proj): Linear(in_features=3072, out_features=768, bias=True) |
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) |
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(ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) |
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) |
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(8): ResidualAttentionBlock( |
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(attn): MultiheadAttention( |
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(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) |
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) |
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(ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) |
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(mlp): Sequential( |
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(c_fc): Linear(in_features=768, out_features=3072, bias=True) |
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(gelu): QuickGELU() |
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(c_proj): Linear(in_features=3072, out_features=768, bias=True) |
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) |
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(ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) |
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) |
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(9): ResidualAttentionBlock( |
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(attn): MultiheadAttention( |
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(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) |
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) |
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(ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) |
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(mlp): Sequential( |
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(c_fc): Linear(in_features=768, out_features=3072, bias=True) |
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(gelu): QuickGELU() |
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(c_proj): Linear(in_features=3072, out_features=768, bias=True) |
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) |
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(ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) |
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) |
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(10): ResidualAttentionBlock( |
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(attn): MultiheadAttention( |
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(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) |
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) |
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(ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) |
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(mlp): Sequential( |
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(c_fc): Linear(in_features=768, out_features=3072, bias=True) |
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(gelu): QuickGELU() |
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(c_proj): Linear(in_features=3072, out_features=768, bias=True) |
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) |
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(ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) |
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) |
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(11): ResidualAttentionBlock( |
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(attn): MultiheadAttention( |
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(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) |
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) |
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(ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) |
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(mlp): Sequential( |
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(c_fc): Linear(in_features=768, out_features=3072, bias=True) |
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(gelu): QuickGELU() |
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(c_proj): Linear(in_features=3072, out_features=768, bias=True) |
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) |
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(ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) |
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) |
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) |
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) |
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(ln_post): LayerNorm((768,), eps=1e-05, elementwise_affine=True) |
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) |
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(transformer): Transformer( |
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(resblocks): Sequential( |
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(0): ResidualAttentionBlock( |
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(attn): MultiheadAttention( |
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(out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) |
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) |
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(ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) |
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(mlp): Sequential( |
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(c_fc): Linear(in_features=512, out_features=2048, bias=True) |
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(gelu): QuickGELU() |
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(c_proj): Linear(in_features=2048, out_features=512, bias=True) |
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) |
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(ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) |
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) |
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(1): ResidualAttentionBlock( |
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(attn): MultiheadAttention( |
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(out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) |
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) |
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(ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) |
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(mlp): Sequential( |
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(c_fc): Linear(in_features=512, out_features=2048, bias=True) |
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(gelu): QuickGELU() |
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(c_proj): Linear(in_features=2048, out_features=512, bias=True) |
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) |
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(ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) |
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) |
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(2): ResidualAttentionBlock( |
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(attn): MultiheadAttention( |
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(out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) |
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) |
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(ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) |
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(mlp): Sequential( |
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(c_fc): Linear(in_features=512, out_features=2048, bias=True) |
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(gelu): QuickGELU() |
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(c_proj): Linear(in_features=2048, out_features=512, bias=True) |
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) |
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(ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) |
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) |
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(3): ResidualAttentionBlock( |
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(attn): MultiheadAttention( |
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(out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) |
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) |
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(ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) |
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(mlp): Sequential( |
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(c_fc): Linear(in_features=512, out_features=2048, bias=True) |
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(gelu): QuickGELU() |
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(c_proj): Linear(in_features=2048, out_features=512, bias=True) |
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) |
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(ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) |
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) |
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(4): ResidualAttentionBlock( |
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(attn): MultiheadAttention( |
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(out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) |
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) |
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(ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) |
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(mlp): Sequential( |
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(c_fc): Linear(in_features=512, out_features=2048, bias=True) |
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(gelu): QuickGELU() |
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(c_proj): Linear(in_features=2048, out_features=512, bias=True) |
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) |
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(ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) |
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) |
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(5): ResidualAttentionBlock( |
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(attn): MultiheadAttention( |
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(out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) |
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) |
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(ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) |
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(mlp): Sequential( |
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(c_fc): Linear(in_features=512, out_features=2048, bias=True) |
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(gelu): QuickGELU() |
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(c_proj): Linear(in_features=2048, out_features=512, bias=True) |
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) |
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(ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) |
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) |
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(6): ResidualAttentionBlock( |
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(attn): MultiheadAttention( |
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(out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) |
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) |
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(ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) |
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(mlp): Sequential( |
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(c_fc): Linear(in_features=512, out_features=2048, bias=True) |
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(gelu): QuickGELU() |
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(c_proj): Linear(in_features=2048, out_features=512, bias=True) |
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) |
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(ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) |
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) |
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(7): ResidualAttentionBlock( |
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(attn): MultiheadAttention( |
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(out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) |
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) |
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(ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) |
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(mlp): Sequential( |
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(c_fc): Linear(in_features=512, out_features=2048, bias=True) |
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(gelu): QuickGELU() |
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(c_proj): Linear(in_features=2048, out_features=512, bias=True) |
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) |
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(ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) |
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) |
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(8): ResidualAttentionBlock( |
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(attn): MultiheadAttention( |
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(out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) |
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) |
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(ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) |
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(mlp): Sequential( |
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(c_fc): Linear(in_features=512, out_features=2048, bias=True) |
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(gelu): QuickGELU() |
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(c_proj): Linear(in_features=2048, out_features=512, bias=True) |
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) |
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(ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) |
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) |
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(9): ResidualAttentionBlock( |
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(attn): MultiheadAttention( |
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(out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) |
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) |
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(ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) |
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(mlp): Sequential( |
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(c_fc): Linear(in_features=512, out_features=2048, bias=True) |
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(gelu): QuickGELU() |
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(c_proj): Linear(in_features=2048, out_features=512, bias=True) |
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) |
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(ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) |
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) |
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(10): ResidualAttentionBlock( |
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(attn): MultiheadAttention( |
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(out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) |
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) |
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(ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) |
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(mlp): Sequential( |
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(c_fc): Linear(in_features=512, out_features=2048, bias=True) |
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(gelu): QuickGELU() |
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(c_proj): Linear(in_features=2048, out_features=512, bias=True) |
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) |
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(ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) |
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) |
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(11): ResidualAttentionBlock( |
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(attn): MultiheadAttention( |
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(out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) |
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) |
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(ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) |
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(mlp): Sequential( |
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(c_fc): Linear(in_features=512, out_features=2048, bias=True) |
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(gelu): QuickGELU() |
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(c_proj): Linear(in_features=2048, out_features=512, bias=True) |
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) |
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(ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) |
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) |
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) |
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) |
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(token_embedding): Embedding(49408, 512) |
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(ln_final): LayerNorm((512,), eps=1e-05, elementwise_affine=True) |
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) |
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) |
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) |
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``` |
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## Citing & Authors |
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This model was trained by [sentence-transformers](https://www.sbert.net/). |
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If you find this model helpful, feel free to cite our publication [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084): |
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```bibtex |
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@inproceedings{reimers-2019-sentence-bert, |
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title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
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author = "Reimers, Nils and Gurevych, Iryna", |
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booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
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month = "11", |
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year = "2019", |
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publisher = "Association for Computational Linguistics", |
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url = "http://arxiv.org/abs/1908.10084", |
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} |
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``` |
