Push model using huggingface_hub.

0_CLIPModel/config.json

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+}

0_CLIPModel/preprocessor_config.json

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+{
+  "crop_size": {
+    "height": 224,
+    "width": 224
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+  "do_center_crop": true,
+  "do_convert_rgb": true,
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "feature_extractor_type": "CLIPFeatureExtractor",
+  "image_mean": [
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+    0.4578275,
+    0.40821073
+  ],
+  "image_processor_type": "CLIPImageProcessor",
+  "image_std": [
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+    0.26130258,
+    0.27577711
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+  "processor_class": "CLIPProcessor",
+  "resample": 3,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "shortest_edge": 224
+  }
+}

0_CLIPModel/pytorch_model.bin

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0_CLIPModel/special_tokens_map.json

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

0_CLIPModel/tokenizer_config.json

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+{
+  "add_prefix_space": false,
+  "bos_token": {
+    "__type": "AddedToken",
+    "content": "<|startoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
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+  "clean_up_tokenization_spaces": true,
+  "do_lower_case": true,
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+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
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+  "errors": "replace",
+  "model_max_length": 77,
+  "pad_token": "<|endoftext|>",
+  "processor_class": "CLIPProcessor",
+  "tokenizer_class": "CLIPTokenizer",
+  "unk_token": {
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+    "content": "<|endoftext|>",
+    "lstrip": false,
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+}

README.md

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+---
+license: apache-2.0
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+pipeline_tag: text-classification
+---
+
+# priyank-m/original-copy-image-classifier
+
+This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
+
+1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
+2. Training a classification head with features from the fine-tuned Sentence Transformer.
+
+## Usage
+
+To use this model for inference, first install the SetFit library:
+
+```bash
+python -m pip install setfit
+```
+
+You can then run inference as follows:
+
+```python
+from setfit import SetFitModel
+
+# Download from Hub and run inference
+model = SetFitModel.from_pretrained("priyank-m/original-copy-image-classifier")
+# Run inference
+preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
+```
+
+## BibTeX entry and citation info
+
+```bibtex
+@article{https://doi.org/10.48550/arxiv.2209.11055,
+doi = {10.48550/ARXIV.2209.11055},
+url = {https://arxiv.org/abs/2209.11055},
+author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
+keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
+title = {Efficient Few-Shot Learning Without Prompts},
+publisher = {arXiv},
+year = {2022},
+copyright = {Creative Commons Attribution 4.0 International}
+}
+```

config_sentence_transformers.json

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

model_head.pkl

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modules.json

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