Add SetFit model

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 384,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": true,
+  "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:
+- f1
+widget:
+- text: What could possibly go wrong?
+- text: We may have faith that human inventiveness will prevail in the long run.
+- text: That can happen again.
+- text: But in fact it was intensely rational.
+- text: Chinese crime, like Chinese cuisine, varies according to regional origin.
+pipeline_tag: text-classification
+inference: true
+model-index:
+- name: SetFit
+  results:
+  - task:
+      type: text-classification
+      name: Text Classification
+    dataset:
+      name: Unknown
+      type: unknown
+      split: test
+    metrics:
+    - type: f1
+      value: 0.7526132404181185
+      name: F1
+---
+
+# SetFit
+
+This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. A SVC 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:** [Unknown](https://huggingface.co/unknown) -->
+- **Classification head:** a SVC instance
+- **Maximum Sequence Length:** 256 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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      |
+|:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| SUBJ  | <ul><li>'Gone are the days when they led the world in recession-busting'</li><li>'Who so mean that he will not himself be taxed, who so mindful of wealth that he will not favor increasing the popular taxes, in aid of these defective children?'</li><li>'That state has sixty-two counties and sixty cities … In addition there are 932 towns, 507 villages, and, at the last count, 9,600 school districts … Just try to render efficient service … amid the diffused identities and inevitable jealousies of, roughly, 11,000 independent administrative officers or boards!'</li></ul> |
+| OBJ   | <ul><li>'Is this a warning of what’s to come?'</li><li>'This unique set of circumstances has brought PCL back into focus as the safe haven of choice for global players seeking somewhere to stash their cash.'</li><li>'Socialists believe that, if everyone cannot have something, no one shall.'</li></ul>                                                                                                                                                                                                                                                                                 |
+
+## Evaluation
+
+### Metrics
+| Label   | F1     |
+|:--------|:-------|
+| **all** | 0.7526 |
+
+## 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("SOUMYADEEPSAR/Setfit_subj_SVC")
+# Run inference
+preds = model("That can happen again.")
+```
+
+<!--
+### 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   | 3   | 35.9834 | 97  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| OBJ   | 117                   |
+| SUBJ  | 124                   |
+
+### Training Hyperparameters
+- batch_size: (8, 8)
+- num_epochs: (5, 5)
+- max_steps: -1
+- sampling_strategy: oversampling
+- num_iterations: 20
+- body_learning_rate: (1e-05, 1e-05)
+- head_learning_rate: 1e-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 |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0008 | 1    | 0.3862        | -               |
+| 0.0415 | 50   | 0.4092        | -               |
+| 0.0830 | 100  | 0.3596        | -               |
+| 0.1245 | 150  | 0.2618        | -               |
+| 0.1660 | 200  | 0.2447        | -               |
+| 0.2075 | 250  | 0.263         | -               |
+| 0.2490 | 300  | 0.2583        | -               |
+| 0.2905 | 350  | 0.3336        | -               |
+| 0.3320 | 400  | 0.2381        | -               |
+| 0.3734 | 450  | 0.2454        | -               |
+| 0.4149 | 500  | 0.259         | -               |
+| 0.4564 | 550  | 0.2083        | -               |
+| 0.4979 | 600  | 0.2437        | -               |
+| 0.5394 | 650  | 0.2231        | -               |
+| 0.5809 | 700  | 0.0891        | -               |
+| 0.6224 | 750  | 0.1164        | -               |
+| 0.6639 | 800  | 0.0156        | -               |
+| 0.7054 | 850  | 0.0394        | -               |
+| 0.7469 | 900  | 0.0065        | -               |
+| 0.7884 | 950  | 0.0024        | -               |
+| 0.8299 | 1000 | 0.0012        | -               |
+| 0.8714 | 1050 | 0.0014        | -               |
+| 0.9129 | 1100 | 0.0039        | -               |
+| 0.9544 | 1150 | 0.0039        | -               |
+| 0.9959 | 1200 | 0.001         | -               |
+| 1.0373 | 1250 | 0.0007        | -               |
+| 1.0788 | 1300 | 0.0003        | -               |
+| 1.1203 | 1350 | 0.001         | -               |
+| 1.1618 | 1400 | 0.0003        | -               |
+| 1.2033 | 1450 | 0.0003        | -               |
+| 1.2448 | 1500 | 0.0014        | -               |
+| 1.2863 | 1550 | 0.0003        | -               |
+| 1.3278 | 1600 | 0.0003        | -               |
+| 1.3693 | 1650 | 0.0001        | -               |
+| 1.4108 | 1700 | 0.0004        | -               |
+| 1.4523 | 1750 | 0.0003        | -               |
+| 1.4938 | 1800 | 0.0008        | -               |
+| 1.5353 | 1850 | 0.0002        | -               |
+| 1.5768 | 1900 | 0.0005        | -               |
+| 1.6183 | 1950 | 0.0002        | -               |
+| 1.6598 | 2000 | 0.0004        | -               |
+| 1.7012 | 2050 | 0.0001        | -               |
+| 1.7427 | 2100 | 0.0002        | -               |
+| 1.7842 | 2150 | 0.0002        | -               |
+| 1.8257 | 2200 | 0.0002        | -               |
+| 1.8672 | 2250 | 0.0003        | -               |
+| 1.9087 | 2300 | 0.0001        | -               |
+| 1.9502 | 2350 | 0.0002        | -               |
+| 1.9917 | 2400 | 0.0001        | -               |
+| 2.0332 | 2450 | 0.0003        | -               |
+| 2.0747 | 2500 | 0.0002        | -               |
+| 2.1162 | 2550 | 0.0001        | -               |
+| 2.1577 | 2600 | 0.0001        | -               |
+| 2.1992 | 2650 | 0.0004        | -               |
+| 2.2407 | 2700 | 0.0002        | -               |
+| 2.2822 | 2750 | 0.0001        | -               |
+| 2.3237 | 2800 | 0.0005        | -               |
+| 2.3651 | 2850 | 0.0002        | -               |
+| 2.4066 | 2900 | 0.0003        | -               |
+| 2.4481 | 2950 | 0.0001        | -               |
+| 2.4896 | 3000 | 0.0001        | -               |
+| 2.5311 | 3050 | 0.0001        | -               |
+| 2.5726 | 3100 | 0.0001        | -               |
+| 2.6141 | 3150 | 0.0002        | -               |
+| 2.6556 | 3200 | 0.0001        | -               |
+| 2.6971 | 3250 | 0.0002        | -               |
+| 2.7386 | 3300 | 0.0002        | -               |
+| 2.7801 | 3350 | 0.0001        | -               |
+| 2.8216 | 3400 | 0.0001        | -               |
+| 2.8631 | 3450 | 0.0001        | -               |
+| 2.9046 | 3500 | 0.0001        | -               |
+| 2.9461 | 3550 | 0.0           | -               |
+| 2.9876 | 3600 | 0.0002        | -               |
+| 3.0290 | 3650 | 0.0001        | -               |
+| 3.0705 | 3700 | 0.0           | -               |
+| 3.1120 | 3750 | 0.0001        | -               |
+| 3.1535 | 3800 | 0.0001        | -               |
+| 3.1950 | 3850 | 0.0001        | -               |
+| 3.2365 | 3900 | 0.0001        | -               |
+| 3.2780 | 3950 | 0.0001        | -               |
+| 3.3195 | 4000 | 0.0001        | -               |
+| 3.3610 | 4050 | 0.0001        | -               |
+| 3.4025 | 4100 | 0.0           | -               |
+| 3.4440 | 4150 | 0.0001        | -               |
+| 3.4855 | 4200 | 0.0001        | -               |
+| 3.5270 | 4250 | 0.0001        | -               |
+| 3.5685 | 4300 | 0.0001        | -               |
+| 3.6100 | 4350 | 0.0002        | -               |
+| 3.6515 | 4400 | 0.0001        | -               |
+| 3.6929 | 4450 | 0.0001        | -               |
+| 3.7344 | 4500 | 0.0           | -               |
+| 3.7759 | 4550 | 0.0           | -               |
+| 3.8174 | 4600 | 0.0001        | -               |
+| 3.8589 | 4650 | 0.0001        | -               |
+| 3.9004 | 4700 | 0.0001        | -               |
+| 3.9419 | 4750 | 0.0           | -               |
+| 3.9834 | 4800 | 0.0001        | -               |
+| 4.0249 | 4850 | 0.0001        | -               |
+| 4.0664 | 4900 | 0.0001        | -               |
+| 4.1079 | 4950 | 0.0001        | -               |
+| 4.1494 | 5000 | 0.0           | -               |
+| 4.1909 | 5050 | 0.0           | -               |
+| 4.2324 | 5100 | 0.0           | -               |
+| 4.2739 | 5150 | 0.0           | -               |
+| 4.3154 | 5200 | 0.0001        | -               |
+| 4.3568 | 5250 | 0.0001        | -               |
+| 4.3983 | 5300 | 0.0001        | -               |
+| 4.4398 | 5350 | 0.0           | -               |
+| 4.4813 | 5400 | 0.0001        | -               |
+| 4.5228 | 5450 | 0.0           | -               |
+| 4.5643 | 5500 | 0.0001        | -               |
+| 4.6058 | 5550 | 0.0001        | -               |
+| 4.6473 | 5600 | 0.0001        | -               |
+| 4.6888 | 5650 | 0.0           | -               |
+| 4.7303 | 5700 | 0.0001        | -               |
+| 4.7718 | 5750 | 0.0001        | -               |
+| 4.8133 | 5800 | 0.0001        | -               |
+| 4.8548 | 5850 | 0.0           | -               |
+| 4.8963 | 5900 | 0.0           | -               |
+| 4.9378 | 5950 | 0.0           | -               |
+| 4.9793 | 6000 | 0.0001        | -               |
+
+### Framework Versions
+- Python: 3.10.12
+- SetFit: 1.0.3
+- Sentence Transformers: 2.7.0
+- Transformers: 4.40.1
+- PyTorch: 2.2.1+cu121
+- Datasets: 2.19.0
+- Tokenizers: 0.19.1
+
+## 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": "sentence-transformers/all-MiniLM-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": 384,
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "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": "4.40.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,9 @@
+{
+  "__version__": {
+    "sentence_transformers": "2.0.0",
+    "transformers": "4.6.1",
+    "pytorch": "1.8.1"
+  },
+  "prompts": {},
+  "default_prompt_name": null
+}

config_setfit.json

@@ -0,0 +1,7 @@
+{
+  "labels": [
+    "OBJ",
+    "SUBJ"
+  ],
+  "normalize_embeddings": false
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5997e058c2bf91bb935638573b833f4282c47b02ce8260bb66560050152f900c
+size 90864192

model_head.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:459974c65f4cd358d28dacd547e3a3dc2a171ce935e6db7bb28cbc688459d99e
+size 57307

modules.json

@@ -0,0 +1,20 @@
+[
+  {
+    "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

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 256,
+  "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,37 @@
+{
+  "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

@@ -0,0 +1,64 @@
+{
+  "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]",
+  "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]"
+}