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multi-label_8_sent_v3_1/1_Pooling/config.json

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+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": true,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false
+}

multi-label_8_sent_v3_1/README.md

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+---
+license: apache-2.0
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+pipeline_tag: text-classification
+---
+
+# multi-label_8_sent_v3_1
+
+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("multi-label_8_sent_v3_1")
+# 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}
+}
+```

multi-label_8_sent_v3_1/config.json

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+  "architectures": [
+    "MPNetModel"
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+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
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+  "model_type": "mpnet",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "relative_attention_num_buckets": 32,
+  "torch_dtype": "float32",
+  "transformers_version": "4.25.1",
+  "vocab_size": 30527
+}

multi-label_8_sent_v3_1/config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "2.0.0",
+    "transformers": "4.7.0",
+    "pytorch": "1.9.0+cu102"
+  }
+}

multi-label_8_sent_v3_1/model_head.pkl

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multi-label_8_sent_v3_1/modules.json

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+    "type": "sentence_transformers.models.Transformer"
+  },
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+    "name": "1",
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+    "type": "sentence_transformers.models.Pooling"
+  }
+]

multi-label_8_sent_v3_1/pytorch_model.bin

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multi-label_8_sent_v3_1/sentence_bert_config.json

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+  "do_lower_case": false
+}

multi-label_8_sent_v3_1/special_tokens_map.json

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multi-label_8_sent_v3_1/tokenizer_config.json

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