Add SetFit model

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
+}

README.md

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+---
+library_name: setfit
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+metrics:
+- accuracy
+widget:
+- text: Someone comes out of the shack and shoves one of the kids to the ground. He
+- text: He approaches the object and reads a plaque on its side. Someone
+- text: Later at someone's family farm, someone sees the lights on in the hangar.
+    Someone
+- text: Someone stands looking over some of the old photographs as someone goes through
+    the mess on the desk. Someone
+- text: Snow blows around a city of towering crystalline structures. A warrior
+pipeline_tag: text-classification
+inference: true
+base_model: sentence-transformers/paraphrase-mpnet-base-v2
+model-index:
+- name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2
+  results:
+  - task:
+      type: text-classification
+      name: Text Classification
+    dataset:
+      name: Unknown
+      type: unknown
+      split: test
+    metrics:
+    - type: accuracy
+      value: 0.12786885245901639
+      name: Accuracy
+---
+
+# SetFit with sentence-transformers/paraphrase-mpnet-base-v2
+
+This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) as the Sentence Transformer embedding model. A [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance is used for 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.
+
+## Model Details
+
+### Model Description
+- **Model Type:** SetFit
+- **Sentence Transformer body:** [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2)
+- **Classification head:** a [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance
+- **Maximum Sequence Length:** 512 tokens
+- **Number of Classes:** 9 classes
+<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
+- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
+- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
+
+### Model Labels
+| Label | Examples                                                                                                                                                                                                                                                                                                                                                                           |
+|:------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 4     | <ul><li>"On the shop floor, his little helper helps himself to an expensive handbag from a display cabinet, then some women's designer shoes, all of which are detailed on a list. He"</li><li>"He finds someone's records in a box. Someone"</li><li>'With a nod, the man hands it over to the defeated boy. Someone'</li></ul>                                                   |
+| 1     | <ul><li>'A lot of people are sitting on terraces in a big field and people is walking in the entrance of a big stadium. men'</li><li>'We see a man dunk the ball twice. We'</li><li>'Several people use different methods to perform trick shots. They continue performing impressive shots'</li></ul>                                                                             |
+| 6     | <ul><li>'A young child is moving back and fourth on a swing while laughing and smiling to the camera. The child'</li><li>'The son of Poseidon holds the water at bay on either side of himself. Someone'</li><li>'The guy pours product in a container and uses a brush to put the liquid on the surface of a metal object. The guy'</li></ul>                                     |
+| 8     | <ul><li>'A woman smiles at the camera. The woman'</li><li>'A girl is shown several times running on a track. She'</li><li>'Someone peers out from the cabin. As she emerges, someone'</li></ul>                                                                                                                                                                                    |
+| 2     | <ul><li>'As our view retracts through the star map a holographic line sets out from the gunner chair and targets hologram of the planet earth. She'</li><li>'Together, they wander a few steps without taking their eyes off of him. Now in the car as someone drives, someone'</li><li>'People stand by the wall, laughing. He'</li></ul>                                         |
+| 0     | <ul><li>'Someone steps outside and opens an umbrella. Someone halts,'</li><li>'He shows a water bottle he has along with a brush, and uses the brush to remove snow from the dash window of a car and the water to remove any excess snow left on the windshield. Once finished, he'</li><li>'She opens a small metal box on a desk and pushes a button inside. Someone'</li></ul> |
+| 5     | <ul><li>'Now in the eating quarters, someone faces a husky, larged - nosed cook. The cook'</li><li>'She forces a smile, then watches him place his hand on her hand. He caresses her cheek, and she'</li><li>'Someone stirs the cookie dough in a bowl. The dough'</li></ul>                                                                                                       |
+| 3     | <ul><li>'A kid in blue shorts is vacuuming the floor. A kid in a red shirt'</li><li>'The official extends a red flag. As Master someone'</li><li>'The girls flips, then runs, flips and dismounts. The cloud'</li></ul>                                                                                                                                                            |
+| 7     | <ul><li>'She flinches, but quickly composes herself and moves on. The crowd of onlookers'</li><li>'He eyes someone with a furrowed brow, then springs up and hurries after her. Someone and someone'</li><li>'Now, someone stands below an overcast sky. Strands of his greasy black hair'</li></ul>                                                                               |
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 0.1279   |
+
+## Uses
+
+### Direct Use for Inference
+
+First install the SetFit library:
+
+```bash
+pip install setfit
+```
+
+Then you can load this model and run inference.
+
+```python
+from setfit import SetFitModel
+
+# Download from the 🤗 Hub
+model = SetFitModel.from_pretrained("HelgeKn/Swag-multi-class-6")
+# Run inference
+preds = model("He approaches the object and reads a plaque on its side. Someone")
+```
+
+<!--
+### Downstream Use
+
+*List how someone could finetune this model on their own dataset.*
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Set Metrics
+| Training set | Min | Median  | Max |
+|:-------------|:----|:--------|:----|
+| Word count   | 6   | 14.3148 | 40  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0     | 6                     |
+| 1     | 6                     |
+| 2     | 6                     |
+| 3     | 6                     |
+| 4     | 6                     |
+| 5     | 6                     |
+| 6     | 6                     |
+| 7     | 6                     |
+| 8     | 6                     |
+
+### Training Hyperparameters
+- batch_size: (16, 16)
+- num_epochs: (2, 2)
+- max_steps: -1
+- sampling_strategy: oversampling
+- num_iterations: 20
+- body_learning_rate: (2e-05, 2e-05)
+- head_learning_rate: 2e-05
+- loss: CosineSimilarityLoss
+- distance_metric: cosine_distance
+- margin: 0.25
+- end_to_end: False
+- use_amp: False
+- warmup_proportion: 0.1
+- seed: 42
+- eval_max_steps: -1
+- load_best_model_at_end: False
+
+### Training Results
+| Epoch  | Step | Training Loss | Validation Loss |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0074 | 1    | 0.303         | -               |
+| 0.3704 | 50   | 0.1185        | -               |
+| 0.7407 | 100  | 0.0656        | -               |
+| 1.1111 | 150  | 0.0179        | -               |
+| 1.4815 | 200  | 0.0109        | -               |
+| 1.8519 | 250  | 0.0076        | -               |
+
+### Framework Versions
+- Python: 3.9.13
+- SetFit: 1.0.1
+- Sentence Transformers: 2.2.2
+- Transformers: 4.36.0
+- PyTorch: 2.1.1+cpu
+- Datasets: 2.15.0
+- Tokenizers: 0.15.0
+
+## Citation
+
+### BibTeX
+```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}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

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+{
+  "_name_or_path": "C:\\Users\\Man_f/.cache\\torch\\sentence_transformers\\sentence-transformers_paraphrase-mpnet-base-v2\\",
+  "architectures": [
+    "MPNetModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "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": "4.36.0",
+  "vocab_size": 30527
+}

config_sentence_transformers.json

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

config_setfit.json

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+{
+  "labels": null,
+  "normalize_embeddings": false
+}

model.safetensors

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+size 437967672

model_head.pkl

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+size 29204

modules.json

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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

sentence_bert_config.json

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+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
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+  "eos_token": {
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+    "lstrip": false,
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+    "single_word": false
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+  "mask_token": {
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+    "lstrip": true,
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+    "single_word": false
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+    "rstrip": false,
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+  },
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+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
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+      "single_word": false,
+      "special": true
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+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "eos_token": "</s>",
+  "mask_token": "<mask>",
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "MPNetTokenizer",
+  "unk_token": "[UNK]"
+}