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clip-ViT-B-32

Sentence Similarity
sentence-transformers
feature-extraction
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clip-ViT-B-32 / README.md
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sentence-transformers/clip-ViT-B-32

This is a sentence-transformers model: It maps sentences & paragraphs to a None dimensional dense vector space and can be used for tasks like clustering or semantic search.

Usage (Sentence-Transformers)

Using this model becomes easy when you have sentence-transformers installed:

pip install -U sentence-transformers

Then you can use the model like this:

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

model = SentenceTransformer('sentence-transformers/clip-ViT-B-32')
embeddings = model.encode(sentences)
print(embeddings)

Evaluation Results

For an automated evaluation of this model, see the Sentence Embeddings Benchmark: https://seb.sbert.net

Full Model Architecture 

SentenceTransformer(
  (0): CLIPModel(
    (model): CLIP(
      (visual): VisualTransformer(
        (conv1): Conv2d(3, 768, kernel_size=(32, 32), stride=(32, 32), bias=False)
        (ln_pre): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
        (transformer): Transformer(
          (resblocks): Sequential(
            (0): ResidualAttentionBlock(
              (attn): MultiheadAttention(
                (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
              )
              (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
              (mlp): Sequential(
                (c_fc): Linear(in_features=768, out_features=3072, bias=True)
                (gelu): QuickGELU()
                (c_proj): Linear(in_features=3072, out_features=768, bias=True)
              )
              (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
            )
            (1): ResidualAttentionBlock(
              (attn): MultiheadAttention(
                (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
              )
              (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
              (mlp): Sequential(
                (c_fc): Linear(in_features=768, out_features=3072, bias=True)
                (gelu): QuickGELU()
                (c_proj): Linear(in_features=3072, out_features=768, bias=True)
              )
              (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
            )
            (2): ResidualAttentionBlock(
              (attn): MultiheadAttention(
                (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
              )
              (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
              (mlp): Sequential(
                (c_fc): Linear(in_features=768, out_features=3072, bias=True)
                (gelu): QuickGELU()
                (c_proj): Linear(in_features=3072, out_features=768, bias=True)
              )
              (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
            )
            (3): ResidualAttentionBlock(
              (attn): MultiheadAttention(
                (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
              )
              (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
              (mlp): Sequential(
                (c_fc): Linear(in_features=768, out_features=3072, bias=True)
                (gelu): QuickGELU()
                (c_proj): Linear(in_features=3072, out_features=768, bias=True)
              )
              (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
            )
            (4): ResidualAttentionBlock(
              (attn): MultiheadAttention(
                (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
              )
              (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
              (mlp): Sequential(
                (c_fc): Linear(in_features=768, out_features=3072, bias=True)
                (gelu): QuickGELU()
                (c_proj): Linear(in_features=3072, out_features=768, bias=True)
              )
              (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
            )
            (5): ResidualAttentionBlock(
              (attn): MultiheadAttention(
                (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
              )
              (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
              (mlp): Sequential(
                (c_fc): Linear(in_features=768, out_features=3072, bias=True)
                (gelu): QuickGELU()
                (c_proj): Linear(in_features=3072, out_features=768, bias=True)
              )
              (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
            )
            (6): ResidualAttentionBlock(
              (attn): MultiheadAttention(
                (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
              )
              (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
              (mlp): Sequential(
                (c_fc): Linear(in_features=768, out_features=3072, bias=True)
                (gelu): QuickGELU()
                (c_proj): Linear(in_features=3072, out_features=768, bias=True)
              )
              (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
            )
            (7): ResidualAttentionBlock(
              (attn): MultiheadAttention(
                (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
              )
              (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
              (mlp): Sequential(
                (c_fc): Linear(in_features=768, out_features=3072, bias=True)
                (gelu): QuickGELU()
                (c_proj): Linear(in_features=3072, out_features=768, bias=True)
              )
              (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
            )
            (8): ResidualAttentionBlock(
              (attn): MultiheadAttention(
                (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
              )
              (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
              (mlp): Sequential(
                (c_fc): Linear(in_features=768, out_features=3072, bias=True)
                (gelu): QuickGELU()
                (c_proj): Linear(in_features=3072, out_features=768, bias=True)
              )
              (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
            )
            (9): ResidualAttentionBlock(
              (attn): MultiheadAttention(
                (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
              )
              (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
              (mlp): Sequential(
                (c_fc): Linear(in_features=768, out_features=3072, bias=True)
                (gelu): QuickGELU()
                (c_proj): Linear(in_features=3072, out_features=768, bias=True)
              )
              (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
            )
            (10): ResidualAttentionBlock(
              (attn): MultiheadAttention(
                (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
              )
              (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
              (mlp): Sequential(
                (c_fc): Linear(in_features=768, out_features=3072, bias=True)
                (gelu): QuickGELU()
                (c_proj): Linear(in_features=3072, out_features=768, bias=True)
              )
              (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
            )
            (11): ResidualAttentionBlock(
              (attn): MultiheadAttention(
                (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
              )
              (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
              (mlp): Sequential(
                (c_fc): Linear(in_features=768, out_features=3072, bias=True)
                (gelu): QuickGELU()
                (c_proj): Linear(in_features=3072, out_features=768, bias=True)
              )
              (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
            )
          )
        )
        (ln_post): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
      )
      (transformer): Transformer(
        (resblocks): Sequential(
          (0): ResidualAttentionBlock(
            (attn): MultiheadAttention(
              (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)
            )
            (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
            (mlp): Sequential(
              (c_fc): Linear(in_features=512, out_features=2048, bias=True)
              (gelu): QuickGELU()
              (c_proj): Linear(in_features=2048, out_features=512, bias=True)
            )
            (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
          )
          (1): ResidualAttentionBlock(
            (attn): MultiheadAttention(
              (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)
            )
            (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
            (mlp): Sequential(
              (c_fc): Linear(in_features=512, out_features=2048, bias=True)
              (gelu): QuickGELU()
              (c_proj): Linear(in_features=2048, out_features=512, bias=True)
            )
            (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
          )
          (2): ResidualAttentionBlock(
            (attn): MultiheadAttention(
              (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)
            )
            (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
            (mlp): Sequential(
              (c_fc): Linear(in_features=512, out_features=2048, bias=True)
              (gelu): QuickGELU()
              (c_proj): Linear(in_features=2048, out_features=512, bias=True)
            )
            (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
          )
          (3): ResidualAttentionBlock(
            (attn): MultiheadAttention(
              (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)
            )
            (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
            (mlp): Sequential(
              (c_fc): Linear(in_features=512, out_features=2048, bias=True)
              (gelu): QuickGELU()
              (c_proj): Linear(in_features=2048, out_features=512, bias=True)
            )
            (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
          )
          (4): ResidualAttentionBlock(
            (attn): MultiheadAttention(
              (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)
            )
            (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
            (mlp): Sequential(
              (c_fc): Linear(in_features=512, out_features=2048, bias=True)
              (gelu): QuickGELU()
              (c_proj): Linear(in_features=2048, out_features=512, bias=True)
            )
            (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
          )
          (5): ResidualAttentionBlock(
            (attn): MultiheadAttention(
              (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)
            )
            (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
            (mlp): Sequential(
              (c_fc): Linear(in_features=512, out_features=2048, bias=True)
              (gelu): QuickGELU()
              (c_proj): Linear(in_features=2048, out_features=512, bias=True)
            )
            (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
          )
          (6): ResidualAttentionBlock(
            (attn): MultiheadAttention(
              (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)
            )
            (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
            (mlp): Sequential(
              (c_fc): Linear(in_features=512, out_features=2048, bias=True)
              (gelu): QuickGELU()
              (c_proj): Linear(in_features=2048, out_features=512, bias=True)
            )
            (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
          )
          (7): ResidualAttentionBlock(
            (attn): MultiheadAttention(
              (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)
            )
            (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
            (mlp): Sequential(
              (c_fc): Linear(in_features=512, out_features=2048, bias=True)
              (gelu): QuickGELU()
              (c_proj): Linear(in_features=2048, out_features=512, bias=True)
            )
            (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
          )
          (8): ResidualAttentionBlock(
            (attn): MultiheadAttention(
              (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)
            )
            (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
            (mlp): Sequential(
              (c_fc): Linear(in_features=512, out_features=2048, bias=True)
              (gelu): QuickGELU()
              (c_proj): Linear(in_features=2048, out_features=512, bias=True)
            )
            (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
          )
          (9): ResidualAttentionBlock(
            (attn): MultiheadAttention(
              (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)
            )
            (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
            (mlp): Sequential(
              (c_fc): Linear(in_features=512, out_features=2048, bias=True)
              (gelu): QuickGELU()
              (c_proj): Linear(in_features=2048, out_features=512, bias=True)
            )
            (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
          )
          (10): ResidualAttentionBlock(
            (attn): MultiheadAttention(
              (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)
            )
            (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
            (mlp): Sequential(
              (c_fc): Linear(in_features=512, out_features=2048, bias=True)
              (gelu): QuickGELU()
              (c_proj): Linear(in_features=2048, out_features=512, bias=True)
            )
            (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
          )
          (11): ResidualAttentionBlock(
            (attn): MultiheadAttention(
              (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)
            )
            (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
            (mlp): Sequential(
              (c_fc): Linear(in_features=512, out_features=2048, bias=True)
              (gelu): QuickGELU()
              (c_proj): Linear(in_features=2048, out_features=512, bias=True)
            )
            (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
          )
        )
      )
      (token_embedding): Embedding(49408, 512)
      (ln_final): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
    )
  )
)

Citing & Authors

This model was trained by sentence-transformers.

If you find this model helpful, feel free to cite our publication Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks:

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "http://arxiv.org/abs/1908.10084",
}