Add SetFit model

1_Pooling/config.json

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+{
+  "word_embedding_dimension": 384,
+  "pooling_mode_cls_token": true,
+  "pooling_mode_mean_tokens": false,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

README.md

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+---
+library_name: setfit
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+metrics:
+- accuracy
+widget:
+- text: Ruukki Group calculates that it has lost EUR 4mn in the failed project .
+- text: The Tecnomen Convergent Charging solution includes functionality for prepaid
+    and post-paid billing , charging and rating of voice calls , video calls , raw
+    data traffic and any type of content services in both mobile and fixed networks
+    .
+- text: The combined value of the planned investments is about EUR 30mn .
+- text: The Diameter Protocol is developed according to the standards IETF RFC 3588
+    and IETF RFC 3539 .
+- text: Below are unaudited consolidated results for Aspocomp Group under IFRS reporting
+    standards .
+pipeline_tag: text-classification
+inference: true
+base_model: BAAI/bge-small-en-v1.5
+model-index:
+- name: SetFit with BAAI/bge-small-en-v1.5
+  results:
+  - task:
+      type: text-classification
+      name: Text Classification
+    dataset:
+      name: Unknown
+      type: unknown
+      split: test
+    metrics:
+    - type: accuracy
+      value: 0.9426048565121413
+      name: Accuracy
+---
+
+# SetFit with BAAI/bge-small-en-v1.5
+
+This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) 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:** [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5)
+- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
+- **Maximum Sequence Length:** 512 tokens
+- **Number of Classes:** 3 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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          |
+|:---------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| positive | <ul><li>'HELSINKI ( AFX ) - Nokian Tyres reported a fourth quarter pretax profit of 61.5 mln eur , up from 48.6 mln on the back of strong sales .'</li><li>'Equity ratio was 60.9 % compared to 54.2 % In the third quarter of 2007 , net sales of the Frozen Foods Business totaled EUR 11.0 , up by about 5 % from the third quarter of 2006 .'</li><li>"`` After a long , unprofitable period the Food Division posted a profitable result , which speaks of a healthier cost structure and a new approach in business operations , '' Rihko said ."</li></ul> |
+| neutral  | <ul><li>'Their names have not yet been released .'</li><li>'The contract includes design , construction , delivery of equipment , installation and commissioning .'</li><li>"Tieto 's service is also used to send , process and receive materials related to absentee voting ."</li></ul>                                                                                                                                                                                                                                                                        |
+| negative | <ul><li>'The company confirmed its estimate for lower revenue for the whole 2009 than the year-ago EUR93 .9 m as given in the interim report on 5 August 2009 .'</li><li>'Acando AB ( ACANB SS ) fell 8.9 percent to 13.35 kronor , the lowest close since Dec. 11 .'</li><li>'Okmetic expects its net sales for the first half of 2009 to be less than in 2008 .'</li></ul>                                                                                                                                                                                      |
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 0.9426   |
+
+## 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("moshew/bge-small-en-v1.5-SetFit-FSA")
+# Run inference
+preds = model("The combined value of the planned investments is about EUR 30mn .")
+```
+
+<!--
+### 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   | 2   | 22.4020 | 60  |
+
+| Label    | Training Sample Count |
+|:---------|:----------------------|
+| negative | 266                   |
+| neutral  | 1142                  |
+| positive | 403                   |
+
+### Training Hyperparameters
+- batch_size: (16, 16)
+- num_epochs: (1, 1)
+- max_steps: -1
+- sampling_strategy: oversampling
+- num_iterations: 10
+- body_learning_rate: (2e-05, 1e-05)
+- head_learning_rate: 0.01
+- 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 |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0004 | 1    | 0.2832        | -               |
+| 0.0221 | 50   | 0.209         | -               |
+| 0.0442 | 100  | 0.1899        | -               |
+| 0.0663 | 150  | 0.1399        | -               |
+| 0.0883 | 200  | 0.1274        | -               |
+| 0.1104 | 250  | 0.0586        | -               |
+| 0.1325 | 300  | 0.0756        | -               |
+| 0.1546 | 350  | 0.0777        | -               |
+| 0.1767 | 400  | 0.0684        | -               |
+| 0.1988 | 450  | 0.0311        | -               |
+| 0.2208 | 500  | 0.0102        | -               |
+| 0.2429 | 550  | 0.052         | -               |
+| 0.2650 | 600  | 0.0149        | -               |
+| 0.2871 | 650  | 0.1042        | -               |
+| 0.3092 | 700  | 0.061         | -               |
+| 0.3313 | 750  | 0.0083        | -               |
+| 0.3534 | 800  | 0.0036        | -               |
+| 0.3754 | 850  | 0.002         | -               |
+| 0.3975 | 900  | 0.0598        | -               |
+| 0.4196 | 950  | 0.0036        | -               |
+| 0.4417 | 1000 | 0.0027        | -               |
+| 0.4638 | 1050 | 0.0617        | -               |
+| 0.4859 | 1100 | 0.0015        | -               |
+| 0.5080 | 1150 | 0.0022        | -               |
+| 0.5300 | 1200 | 0.0016        | -               |
+| 0.5521 | 1250 | 0.0009        | -               |
+| 0.5742 | 1300 | 0.0013        | -               |
+| 0.5963 | 1350 | 0.0009        | -               |
+| 0.6184 | 1400 | 0.0015        | -               |
+| 0.6405 | 1450 | 0.0018        | -               |
+| 0.6625 | 1500 | 0.0015        | -               |
+| 0.6846 | 1550 | 0.0018        | -               |
+| 0.7067 | 1600 | 0.0016        | -               |
+| 0.7288 | 1650 | 0.0022        | -               |
+| 0.7509 | 1700 | 0.0013        | -               |
+| 0.7730 | 1750 | 0.0108        | -               |
+| 0.7951 | 1800 | 0.0016        | -               |
+| 0.8171 | 1850 | 0.0021        | -               |
+| 0.8392 | 1900 | 0.002         | -               |
+| 0.8613 | 1950 | 0.0015        | -               |
+| 0.8834 | 2000 | 0.0016        | -               |
+| 0.9055 | 2050 | 0.0028        | -               |
+| 0.9276 | 2100 | 0.0013        | -               |
+| 0.9496 | 2150 | 0.0019        | -               |
+| 0.9717 | 2200 | 0.0075        | -               |
+| 0.9938 | 2250 | 0.0015        | -               |
+
+### Framework Versions
+- Python: 3.10.12
+- SetFit: 1.0.3
+- Sentence Transformers: 2.5.1
+- Transformers: 4.38.1
+- PyTorch: 2.1.0+cu121
+- Datasets: 2.18.0
+- Tokenizers: 0.15.2
+
+## 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": "BAAI/bge-small-en-v1.5",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "id2label": {
+    "0": "LABEL_0"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "label2id": {
+    "LABEL_0": 0
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.38.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "2.2.2",
+    "transformers": "4.28.1",
+    "pytorch": "1.13.0+cu117"
+  },
+  "prompts": {},
+  "default_prompt_name": null
+}

config_setfit.json

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+{
+  "labels": [
+    "negative",
+    "neutral",
+    "positive"
+  ],
+  "normalize_embeddings": false
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:11dab720450d4492ff84ed3baa65fa60b66e8bb0a83348fcc02244a798bf2dda
+size 133462128

model_head.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d8595235eb279767ad54f331847212692ff3d3553ed44fbe7c3f001bd2eb53bb
+size 10159

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"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

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

special_tokens_map.json

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+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "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": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}