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: part-of-speech ( pos ) tagging is a fundamental language analysis task---part-of-speech
+    ( pos ) tagging is a fundamental nlp task , used by a wide variety of applications
+- text: the two baseline methods were implemented using scikit-learn in python---the
+    models were implemented using scikit-learn module
+- text: semantic parsing is the task of converting a sentence into a representation
+    of its meaning , usually in a logical form grounded in the symbols of some fixed
+    ontology or relational database ( cite-p-21-3-3 , cite-p-21-3-4 , cite-p-21-1-11
+    )---for this language model , we built a trigram language model with kneser-ney
+    smoothing using srilm from the same automatically segmented corpus
+- text: the results show that our model can clearly outperform the baselines in terms
+    of three evaluation metrics---for the extractive or abstractive summaries , we
+    use rouge scores , a metric used to evaluate automatic summarization performance
+    , to measure the pairwise agreement of summaries from different annotators
+- text: language models were built with srilm , modified kneser-ney smoothing , default
+    pruning , and order 5---the language model used was a 5-gram with modified kneserney
+    smoothing , built with srilm toolkit
+pipeline_tag: text-classification
+inference: true
+base_model: sentence-transformers/paraphrase-TinyBERT-L6-v2
+---
+
+# SetFit with sentence-transformers/paraphrase-TinyBERT-L6-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-TinyBERT-L6-v2](https://huggingface.co/sentence-transformers/paraphrase-TinyBERT-L6-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) 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-TinyBERT-L6-v2](https://huggingface.co/sentence-transformers/paraphrase-TinyBERT-L6-v2)
+- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
+- **Maximum Sequence Length:** 128 tokens
+- **Number of Classes:** 2 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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 |
+|:------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 0     | <ul><li>'the defacto standard metric in machine translation is bleu---from character representations , we propose to generate vector representations of entire tweets from characters in our tweet2vec model'</li><li>'arabic is a highly inflectional language with 85 % of words derived from trilateral roots ( alfedaghi and al-anzi 1989 )---chen et al derive bilingual subtree constraints with auto-parsed source-language sentences'</li><li>'labeling of sentence boundaries is a necessary prerequisite for many natural language processing tasks , including part-of-speech tagging and sentence alignment---we have proposed a model for video description which uses neural networks for the entire pipeline from pixels to sentences'</li></ul>                                          |
+| 1     | <ul><li>'in this paper , we present a comprehensive analysis of the relationship between personal traits and brand preferences---in previous research , in this study , we want to systematically investigate the relationship between a comprehensive set of personal traits and brand preferences'</li><li>'the 50-dimensional pre-trained word embeddings are provided by glove , which are fixed during our model training---we use glove vectors with 200 dimensions as pre-trained word embeddings , which are tuned during training'</li><li>'we use glove vectors with 100 dimensions trained on wikipedia and gigaword as word embeddings , which we do not optimize during training---we use glove vectors with 100 dimensions trained on wikipedia and gigaword as word embeddings'</li></ul> |
+
+## 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("whateverweird17/parasci3_1")
+# Run inference
+preds = model("the two baseline methods were implemented using scikit-learn in python---the models were implemented using scikit-learn module")
+```
+
+<!--
+### 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   | 27  | 35.8125 | 54  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0     | 8                     |
+| 1     | 8                     |
+
+### Training Hyperparameters
+- batch_size: (8, 8)
+- num_epochs: (10, 10)
+- max_steps: -1
+- sampling_strategy: oversampling
+- num_iterations: 10
+- 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 |
+|:-----:|:----:|:-------------:|:---------------:|
+| 0.025 | 1    | 0.1715        | -               |
+| 1.25  | 50   | 0.0028        | -               |
+| 2.5   | 100  | 0.0005        | -               |
+| 3.75  | 150  | 0.0002        | -               |
+| 5.0   | 200  | 0.0003        | -               |
+| 6.25  | 250  | 0.0001        | -               |
+| 7.5   | 300  | 0.0002        | -               |
+| 8.75  | 350  | 0.0001        | -               |
+| 10.0  | 400  | 0.0001        | -               |
+
+### Framework Versions
+- Python: 3.10.12
+- SetFit: 1.0.1
+- Sentence Transformers: 2.2.2
+- Transformers: 4.33.0
+- PyTorch: 2.0.0
+- Datasets: 2.16.0
+- Tokenizers: 0.13.3
+
+## 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": "/root/.cache/torch/sentence_transformers/sentence-transformers_paraphrase-TinyBERT-L6-v2/",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 6,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.33.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

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_head.pkl

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+oid sha256:f6fb4290f7d09e8d16a45ab7cfcc0fdc6e315c00b65abee77e9de03a45ba011a
+size 6991

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"
+  }
+]

pytorch_model.bin

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+oid sha256:31c0d56a2b0e8c03eceb17327b08dbc609e29a569251fa55541985b3b2594e81
+size 267854313

sentence_bert_config.json

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+{
+  "max_seq_length": 128,
+  "do_lower_case": false
+}

special_tokens_map.json

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+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

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+{
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "max_length": 128,
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_to_multiple_of": null,
+  "pad_token": "[PAD]",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "[SEP]",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}