Add SetFit model

1_Pooling/config.json

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+{
+  "word_embedding_dimension": 1024,
+  "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
+datasets:
+- dvilasuero/banking77-topics-setfit
+metrics:
+- accuracy
+widget:
+- text: I requested a refund, and never received it. What can I do?
+- text: I have a 1 euro fee on my statement.
+- text: I would like an account for my children, how do I go about doing this?
+- text: What do I need to do to transfer money into my account?
+- text: Which country's currency do you support?
+pipeline_tag: text-classification
+inference: true
+base_model: thenlper/gte-large
+model-index:
+- name: SetFit with thenlper/gte-large
+  results:
+  - task:
+      type: text-classification
+      name: Text Classification
+    dataset:
+      name: dvilasuero/banking77-topics-setfit
+      type: dvilasuero/banking77-topics-setfit
+      split: test
+    metrics:
+    - type: accuracy
+      value: 0.9230769230769231
+      name: Accuracy
+---
+
+# SetFit with thenlper/gte-large
+
+This is a [SetFit](https://github.com/huggingface/setfit) model trained on the [dvilasuero/banking77-topics-setfit](https://huggingface.co/datasets/dvilasuero/banking77-topics-setfit) dataset that can be used for Text Classification. This SetFit model uses [thenlper/gte-large](https://huggingface.co/thenlper/gte-large) 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:** [thenlper/gte-large](https://huggingface.co/thenlper/gte-large)
+- **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:** 8 classes
+- **Training Dataset:** [dvilasuero/banking77-topics-setfit](https://huggingface.co/datasets/dvilasuero/banking77-topics-setfit)
+<!-- - **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                                                                                                                                                                                                                                                                                          |
+|:------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 2     | <ul><li>'The  money I transferred does not show in the balance.'</li><li>'I was wondering how I could have two charges for the same item happen more than once in a 7 day period. Is there anyway I could get this corrected asap.'</li><li>'What is the source of my available funds?'</li></ul> |
+| 0     | <ul><li>'Do you support the EU?'</li><li>"Can you freeze my account?  I just saw there are transactions on my account that I don't recognize.  How can I fix this?"</li><li>'Please close my account. I am unsatisfied with your service.'</li></ul>                                              |
+| 5     | <ul><li>'Are you able to unblock my pin?'</li><li>'I can not find my card pin.'</li><li>'If I need a PIN for my card, where is it located?'</li></ul>                                                                                                                                             |
+| 1     | <ul><li>"I can't get money out of the ATM"</li><li>'Where can I use this card at an ATM?'</li><li>'Can I use my card at any ATMs?'</li></ul>                                                                                                                                                      |
+| 3     | <ul><li>'Can I get cash with this card anywhere?'</li><li>'Can you please show me where I can find the location to link my card?'</li><li>'Am I able to get a card in EU?'</li></ul>                                                                                                              |
+| 6     | <ul><li>'My friends want to top up my account'</li><li>'Can I be topped up once I hit a certain balance?'</li><li>'Can you tell me why my top up was reverted?'</li></ul>                                                                                                                         |
+| 7     | <ul><li>'How do I send my account money through transfer?'</li><li>'How do I transfer money to my account?'</li><li>'How can I transfer money from an outside bank?'</li></ul>                                                                                                                    |
+| 4     | <ul><li>'Do you work with all fiat currencies?'</li><li>'Can I exchange to EUR?'</li><li>'Is my country supported'</li></ul>                                                                                                                                                                      |
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 0.9231   |
+
+## 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("HarshalBhg/gte-large-setfit-train-test2")
+# Run inference
+preds = model("I have a 1 euro fee on my statement.")
+```
+
+<!--
+### 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   | 4   | 10.5833 | 40  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0     | 10                    |
+| 1     | 19                    |
+| 2     | 28                    |
+| 3     | 36                    |
+| 4     | 13                    |
+| 5     | 14                    |
+| 6     | 15                    |
+| 7     | 21                    |
+
+### Training Hyperparameters
+- batch_size: (16, 16)
+- num_epochs: (1, 1)
+- max_steps: -1
+- sampling_strategy: oversampling
+- num_iterations: 20
+- 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 |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0026 | 1    | 0.3183        | -               |
+| 0.1282 | 50   | 0.0614        | -               |
+| 0.2564 | 100  | 0.0044        | -               |
+| 0.3846 | 150  | 0.001         | -               |
+| 0.5128 | 200  | 0.0008        | -               |
+| 0.6410 | 250  | 0.001         | -               |
+| 0.7692 | 300  | 0.0006        | -               |
+| 0.8974 | 350  | 0.0012        | -               |
+
+### Framework Versions
+- Python: 3.10.12
+- SetFit: 1.0.1
+- Sentence Transformers: 2.2.2
+- Transformers: 4.35.2
+- PyTorch: 2.1.0+cu121
+- 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": "/root/.cache/torch/sentence_transformers/thenlper_gte-large/",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.35.2",
+  "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": "4.35.2",
+    "pytorch": "2.1.0+cu121"
+  }
+}

config_setfit.json

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

model.safetensors

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+oid sha256:fc3c32568d9df4c4e88426d14efcfd9071cfb90e9f592466bda41f85b7bf5da6
+size 1340612432

model_head.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6ca4fb5e35421aeaf8fbc060fe40511d66674e53e544b703459636bf370afc28
+size 66487

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": 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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+{
+  "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_lower_case": true,
+  "mask_token": "[MASK]",
+  "max_length": 128,
+  "model_max_length": 1000000000000000019884624838656,
+  "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]"
+}