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0_CLIPModel/config.json

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+{
+  "_name_or_path": "openai/clip-vit-large-patch14",
+  "architectures": [
+    "CLIPModel"
+  ],
+  "initializer_factor": 1.0,
+  "logit_scale_init_value": 2.6592,
+  "model_type": "clip",
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+    "transformers_version": "4.18.0",
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+    "vocab_size": 49408
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+    "intermediate_size": 3072,
+    "num_attention_heads": 12,
+    "num_hidden_layers": 12
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+  "torch_dtype": "float32",
+  "transformers_version": null,
+  "vision_config": {
+    "_name_or_path": "",
+    "add_cross_attention": false,
+    "architectures": null,
+    "attention_dropout": 0.0,
+    "bad_words_ids": null,
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+    "diversity_penalty": 0.0,
+    "do_sample": false,
+    "dropout": 0.0,
+    "early_stopping": false,
+    "encoder_no_repeat_ngram_size": 0,
+    "eos_token_id": null,
+    "exponential_decay_length_penalty": null,
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+    "hidden_act": "quick_gelu",
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+    "id2label": {
+      "0": "LABEL_0",
+      "1": "LABEL_1"
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+    "initializer_factor": 1.0,
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+    "model_type": "clip_vision_model",
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+    "num_attention_heads": 16,
+    "num_beam_groups": 1,
+    "num_beams": 1,
+    "num_hidden_layers": 24,
+    "num_return_sequences": 1,
+    "output_attentions": false,
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+    "patch_size": 14,
+    "prefix": null,
+    "problem_type": null,
+    "pruned_heads": {},
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+    "repetition_penalty": 1.0,
+    "return_dict": true,
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+    "sep_token_id": null,
+    "task_specific_params": null,
+    "temperature": 1.0,
+    "tie_encoder_decoder": false,
+    "tie_word_embeddings": true,
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+    "top_k": 50,
+    "top_p": 1.0,
+    "torch_dtype": null,
+    "torchscript": false,
+    "transformers_version": "4.18.0",
+    "typical_p": 1.0,
+    "use_bfloat16": false
+  },
+  "vision_config_dict": {
+    "hidden_size": 1024,
+    "intermediate_size": 4096,
+    "num_attention_heads": 16,
+    "num_hidden_layers": 24,
+    "patch_size": 14
+  }
+}

0_CLIPModel/preprocessor_config.json

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+{
+  "crop_size": 224,
+  "do_center_crop": true,
+  "do_normalize": true,
+  "do_resize": true,
+  "feature_extractor_type": "CLIPFeatureExtractor",
+  "image_mean": [
+    0.48145466,
+    0.4578275,
+    0.40821073
+  ],
+  "image_std": [
+    0.26862954,
+    0.26130258,
+    0.27577711
+  ],
+  "processor_class": "CLIPProcessor",
+  "resample": 3,
+  "size": 224
+}

0_CLIPModel/pytorch_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e33cd4b4a78c8a2f4b361665db761ab9971705bbd27706ee9299889dffd586ef
+size 1710660257

0_CLIPModel/special_tokens_map.json

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+{"bos_token": {"content": "<|startoftext|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, "eos_token": {"content": "<|endoftext|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, "unk_token": {"content": "<|endoftext|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, "pad_token": "<|endoftext|>"}

0_CLIPModel/tokenizer_config.json

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+{"unk_token": {"content": "<|endoftext|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "bos_token": {"content": "<|startoftext|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "eos_token": {"content": "<|endoftext|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "pad_token": "<|endoftext|>", "add_prefix_space": false, "errors": "replace", "do_lower_case": true, "name_or_path": "openai/clip-vit-large-patch14", "model_max_length": 77, "special_tokens_map_file": "./special_tokens_map.json", "tokenizer_class": "CLIPTokenizer", "processor_class": "CLIPProcessor"}

README.md

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+---
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+---
+
+# clip-ViT-L-14
+
+This is the Image & Text model [CLIP](https://arxiv.org/abs/2103.00020), which maps text and images to a shared vector space. For applications of the models, have a look in our documentation [SBERT.net - Image Search](https://www.sbert.net/examples/applications/image-search/README.html)
+
+## Usage
+
+After installing [sentence-transformers](https://sbert.net) (`pip install sentence-transformers`), the usage of this model is easy:
+
+  
+```python
+from sentence_transformers import SentenceTransformer, util
+from PIL import Image
+
+#Load CLIP model
+model = SentenceTransformer('clip-ViT-L-14')
+
+#Encode an image:
+img_emb = model.encode(Image.open('two_dogs_in_snow.jpg'))
+
+#Encode text descriptions
+text_emb = model.encode(['Two dogs in the snow', 'A cat on a table', 'A picture of London at night'])
+
+#Compute cosine similarities 
+cos_scores = util.cos_sim(img_emb, text_emb)
+print(cos_scores)
+```
+
+See our [SBERT.net - Image Search](https://www.sbert.net/examples/applications/image-search/README.html) documentation for more examples how the model can be used for image search, zero-shot image classification, image clustering and image deduplication.
+
+## Performance
+
+In the following table we find the zero-shot ImageNet validation set accuracy:
+
+| Model | Top 1 Performance |
+| --- | :---: |
+| [clip-ViT-B-32](https://huggingface.co/sentence-transformers/clip-ViT-B-32) | 63.3 |
+| [clip-ViT-B-16](https://huggingface.co/sentence-transformers/clip-ViT-B-16) | 68.1 |
+| [clip-ViT-L-14](https://huggingface.co/sentence-transformers/clip-ViT-L-14) | 75.4 |
+
+For a multilingual version of the CLIP model for 50+ languages have a look at: [clip-ViT-B-32-multilingual-v1](https://huggingface.co/sentence-transformers/clip-ViT-B-32-multilingual-v1)

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "2.2.0",
+    "transformers": "4.18.0",
+    "pytorch": "1.11.0"
+  }
+}

modules.json

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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "0_CLIPModel",
+    "type": "sentence_transformers.models.CLIPModel"
+  }
+]