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:
+- Ramyashree/Dataset-500-validation
+metrics:
+- accuracy
+widget:
+- text: i want to know the status of my reimbursement, how do i track it?
+- text: ask an agent how to modify my profile
+- text: I want to use my other online account, help me switch them
+- text: I want information about your money back policy
+- text: how can I switch to another account?
+pipeline_tag: text-classification
+inference: true
+base_model: thenlper/gte-large
+---
+
+# SetFit with thenlper/gte-large
+
+This is a [SetFit](https://github.com/huggingface/setfit) model trained on the [Ramyashree/Dataset-500-validation](https://huggingface.co/datasets/Ramyashree/Dataset-500-validation) 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:** 10 classes
+- **Training Dataset:** [Ramyashree/Dataset-500-validation](https://huggingface.co/datasets/Ramyashree/Dataset-500-validation)
+<!-- - **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                                                                                                                                                                                                                               |
+|:--------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| create_account      | <ul><li>'can i register?'</li><li>'i have no account, what do i have to do?'</li><li>'i watn to know if i can register two profiles with the same email address'</li></ul>                                                             |
+| delete_account      | <ul><li>'I changed my mind, what should I do to cancel my profile?'</li><li>'i changed my mind, what do i have to do to delete my account?'</li><li>"I odn't want my user account, how do I delete it?"</li></ul>                      |
+| edit_account        | <ul><li>'I want to change my profile, how can I do it?'</li><li>'I need help making changes to my profile'</li><li>'can I make changes to my profile?'</li></ul>                                                                       |
+| recover_password    | <ul><li>'could u ask an agent if i could retrieve my password?'</li><li>'my online account was hacked, how do I recover it?'</li><li>'my account was hacked, can u recover it?'</li></ul>                                              |
+| track_refund        | <ul><li>'can yoy tell me about the status of my reimbursement?'</li><li>'tell me if my reimbursement was processed'</li><li>'I want to view the status of my refund, what can I do?'</li></ul>                                         |
+| check_refund_policy | <ul><li>'I want to check your reimbursement policy, what can I do?'</li><li>'cam u ask an agent if i can see their money back guarantee?'</li><li>'I want to check your refund policy'</li></ul>                                       |
+| switch_account      | <ul><li>'I weant to use my other  account, switch them'</li><li>'ask an agent if i can change to another user account'</li><li>'where to change to another profile'</li></ul>                                                          |
+| payment_issue       | <ul><li>'I have a problem when trying to pay for my online order, notify it'</li><li>'could you ask an agent where I can report issues making a payment, please?'</li><li>'ask an agent how i can inform of problems paying'</li></ul> |
+| get_refund          | <ul><li>'the concert was postponed and i want to get a reimbursement'</li><li>'the concert was postponed, help me get a reimbursement'</li><li>'how to get a reimbursement'</li></ul>                                                  |
+| get_invoice         | <ul><li>'I want to request some bills, can you tell me how I can do it?'</li><li>'ask an agent how I can request somebills'</li><li>'i want to see a bill'</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("Ramyashree/gte-large-with500records-validate")
+# Run inference
+preds = model("how can I switch to another account?")
+```
+
+<!--
+### 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   | 10.356 | 25  |
+
+| Label               | Training Sample Count |
+|:--------------------|:----------------------|
+| check_refund_policy | 50                    |
+| create_account      | 50                    |
+| delete_account      | 50                    |
+| edit_account        | 50                    |
+| get_invoice         | 50                    |
+| get_refund          | 50                    |
+| payment_issue       | 50                    |
+| recover_password    | 50                    |
+| switch_account      | 50                    |
+| track_refund        | 50                    |
+
+### Training Hyperparameters
+- batch_size: (16, 16)
+- num_epochs: (1, 1)
+- 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 |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0008 | 1    | 0.3184        | -               |
+| 0.04   | 50   | 0.1532        | -               |
+| 0.08   | 100  | 0.0078        | -               |
+| 0.12   | 150  | 0.0124        | -               |
+| 0.16   | 200  | 0.0017        | -               |
+| 0.2    | 250  | 0.0009        | -               |
+| 0.24   | 300  | 0.0008        | -               |
+| 0.28   | 350  | 0.0008        | -               |
+| 0.32   | 400  | 0.0007        | -               |
+| 0.36   | 450  | 0.0008        | -               |
+| 0.4    | 500  | 0.0004        | -               |
+| 0.44   | 550  | 0.0005        | -               |
+| 0.48   | 600  | 0.0004        | -               |
+| 0.52   | 650  | 0.0005        | -               |
+| 0.56   | 700  | 0.0003        | -               |
+| 0.6    | 750  | 0.0004        | -               |
+| 0.64   | 800  | 0.0003        | -               |
+| 0.68   | 850  | 0.0003        | -               |
+| 0.72   | 900  | 0.0003        | -               |
+| 0.76   | 950  | 0.0004        | -               |
+| 0.8    | 1000 | 0.0004        | -               |
+| 0.84   | 1050 | 0.0004        | -               |
+| 0.88   | 1100 | 0.0002        | -               |
+| 0.92   | 1150 | 0.0002        | -               |
+| 0.96   | 1200 | 0.0003        | -               |
+| 1.0    | 1250 | 0.0004        | -               |
+
+### 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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+{
+  "normalize_embeddings": false,
+  "labels": null
+}

model.safetensors

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+size 1340612432

model_head.pkl

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+oid sha256:ac6cf43e22df3bc631807ed9354a3342c0f2d22b83bdad76715ba37cc5ec91f8
+size 83591

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
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+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
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+      "normalized": false,
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+      "single_word": false,
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+      "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]"
+}