Add SetFit model

1_Pooling/config.json

@@ -0,0 +1,7 @@
+{
+  "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: Please email the information to me.
+- text: Give me a second, please.
+- text: Is it possible to talk to a higher authority?
+- text: Sorry, too busy to chat right now.
+- text: I already own one, thanks.
+pipeline_tag: text-classification
+inference: true
+base_model: sentence-transformers/paraphrase-mpnet-base-v2
+model-index:
+- name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2
+  results:
+  - task:
+      type: text-classification
+      name: Text Classification
+    dataset:
+      name: Unknown
+      type: unknown
+      split: test
+    metrics:
+    - type: accuracy
+      value: 0.9333333333333333
+      name: Accuracy
+---
+
+# SetFit with sentence-transformers/paraphrase-mpnet-base-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-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-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-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2)
+- **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:** 25 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                                                                                                                                                                                                  |
+|:---------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| do_not_qualify             | <ul><li>"Your target age group doesn't include me."</li><li>"I'm outside the age range for this."</li><li>"I'm not in the age group you're looking for."</li></ul>                                        |
+| can_you_email              | <ul><li>'I prefer email, can you write to me?'</li><li>'Email is more convenient for me, can you use that?'</li><li>'Can you send me the details by email?'</li></ul>                                     |
+| say_again                  | <ul><li>'Can you repeat that, please?'</li><li>'I missed that, can you say it again?'</li><li>'Could you please repeat what you just said?'</li></ul>                                                     |
+| hold_a_sec                 | <ul><li>'One moment, please hold.'</li><li>'Hang on for a bit, please.'</li><li>'Just a minute, please.'</li></ul>                                                                                        |
+| language_barrier           | <ul><li>'English is hard for me, ¿puedo hablar en español?'</li><li>'I struggle with English, ¿puede ser en español?'</li><li>"I'm more comfortable in Spanish, ¿podemos continuar en español?"</li></ul> |
+| decline                    | <ul><li>'wrong'</li><li>'Never'</li><li>"I don't want this, thank you."</li></ul>                                                                                                                         |
+| transfer_request           | <ul><li>'Can you transfer this call to your superior?'</li><li>'I need to speak with someone in charge.'</li><li>'Can I speak with your manager?'</li></ul>                                               |
+| scam                       | <ul><li>"I'm skeptical, this doesn't sound right."</li><li>"I'm wary, this feels like a scam."</li><li>"Are you sure this isn't a scam?"</li></ul>                                                        |
+| who_are_you                | <ul><li>"I would like to know who's calling."</li><li>"Who's calling, please?"</li><li>'Who are you and why are you calling?'</li></ul>                                                                   |
+| where_did_you_get_my_info  | <ul><li>'Can you explain how you got my contact info?'</li><li>"What's the source of my details you have?"</li><li>"I didn't give you my number, where did you get it?"</li></ul>                         |
+| do_not_call                | <ul><li>"Stop calling me, it's annoying!"</li><li>"I don't want to be contacted again."</li><li>"Enough calls, I'm not interested!"</li></ul>                                                             |
+| where_are_you_calling_from | <ul><li>'Where are you calling from?'</li><li>'From which city or country are you calling?'</li><li>'Could you inform me of your current location?'</li></ul>                                             |
+| complain_calls             | <ul><li>"Too many calls like this, it's irritating."</li><li>"I've had several calls like this, it's annoying."</li><li>"I keep getting these calls, it's too much."</li></ul>                            |
+| busy                       | <ul><li>"Right now isn't good, I'm busy with something."</li><li>"I'm swamped at the moment, sorry."</li><li>"I'm busy right now, can't talk."</li></ul>                                                  |
+| greetings                  | <ul><li>'Hi, how can I help you?'</li><li>'Hello, what can I help you with today?'</li><li>'Hello, yes?'</li></ul>                                                                                        |
+| sorry_greeting             | <ul><li>"I'm not at my best, what do you need?"</li><li>"Sorry, it's a bad time, I'm sick."</li><li>"Not a great time, I'm dealing with a personal issue."</li></ul>                                      |
+| GreetBack                  | <ul><li>'Doing well, how about yourself?'</li><li>'Pretty good, what about you?'</li><li>"Not bad, and how's it going on your end?"</li></ul>                                                             |
+| calling_about              | <ul><li>'Why are you calling me?'</li><li>"What's the matter, why the call?"</li><li>'May I know the reason for your call?'</li></ul>                                                                     |
+| answering_machine          | <ul><li>"Leave a message and I'll get back to you."</li><li>"You're speaking to an answering machine, leave a message."</li><li>"This is an answering machine, I'm not available."</li></ul>              |
+| weather                    | <ul><li>"Sunny skies here, what's it like where you are?"</li><li>"It's a bit cloudy here, is it the same there?"</li><li>"It's warm here, what about where you are?"</li></ul>                           |
+| are_you_bot                | <ul><li>'Is this a bot calling me?'</li><li>'Is this a recorded message or are you real?'</li><li>'Are you a live person or a recording?'</li></ul>                                                       |
+| affirmation                | <ul><li>'yes'</li><li>"That's true, yes."</li><li>"Precisely, that's right."</li></ul>                                                                                                                    |
+| not_interested             | <ul><li>"This doesn't interest me, sorry."</li><li>"This offer isn't relevant to my interests."</li><li>"Thanks, but this isn't something I need."</li></ul>                                              |
+| already                    | <ul><li>"I've made this purchase before."</li><li>"This isn't new to me, I have it already."</li><li>"I've been using this for a while now."</li></ul>                                                    |
+| abusibve                   | <ul><li>"This is unacceptable, I won't tolerate this!"</li><li>'I demand you stop this abusive calling!'</li><li>"Stop calling me, it's harassment!"</li></ul>                                            |
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 0.9333   |
+
+## 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("setfit_model_id")
+# Run inference
+preds = model("Give me a second, please.")
+```
+
+<!--
+### 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   | 1   | 6.8375 | 13  |
+
+| Label                      | Training Sample Count |
+|:---------------------------|:----------------------|
+| GreetBack                  | 9                     |
+| abusibve                   | 9                     |
+| affirmation                | 10                    |
+| already                    | 10                    |
+| answering_machine          | 8                     |
+| are_you_bot                | 8                     |
+| busy                       | 9                     |
+| calling_about              | 8                     |
+| can_you_email              | 11                    |
+| complain_calls             | 11                    |
+| decline                    | 10                    |
+| do_not_call                | 12                    |
+| do_not_qualify             | 9                     |
+| greetings                  | 8                     |
+| hold_a_sec                 | 8                     |
+| language_barrier           | 10                    |
+| not_interested             | 11                    |
+| say_again                  | 12                    |
+| scam                       | 9                     |
+| sorry_greeting             | 9                     |
+| transfer_request           | 8                     |
+| weather                    | 10                    |
+| where_are_you_calling_from | 9                     |
+| where_did_you_get_my_info  | 11                    |
+| who_are_you                | 11                    |
+
+### Training Hyperparameters
+- batch_size: (8, 8)
+- num_epochs: (3, 3)
+- 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    | 0.1054        | -               |
+| 0.0417 | 50   | 0.1111        | -               |
+| 0.0833 | 100  | 0.0798        | -               |
+| 0.125  | 150  | 0.0826        | -               |
+| 0.1667 | 200  | 0.0308        | -               |
+| 0.2083 | 250  | 0.0324        | -               |
+| 0.25   | 300  | 0.0607        | -               |
+| 0.2917 | 350  | 0.0042        | -               |
+| 0.3333 | 400  | 0.0116        | -               |
+| 0.375  | 450  | 0.0049        | -               |
+| 0.4167 | 500  | 0.0154        | -               |
+| 0.4583 | 550  | 0.0158        | -               |
+| 0.5    | 600  | 0.0036        | -               |
+| 0.5417 | 650  | 0.001         | -               |
+| 0.5833 | 700  | 0.0015        | -               |
+| 0.625  | 750  | 0.0012        | -               |
+| 0.6667 | 800  | 0.0009        | -               |
+| 0.7083 | 850  | 0.0008        | -               |
+| 0.75   | 900  | 0.0008        | -               |
+| 0.7917 | 950  | 0.0014        | -               |
+| 0.8333 | 1000 | 0.0005        | -               |
+| 0.875  | 1050 | 0.0027        | -               |
+| 0.9167 | 1100 | 0.0007        | -               |
+| 0.9583 | 1150 | 0.0008        | -               |
+| 1.0    | 1200 | 0.0012        | -               |
+| 1.0417 | 1250 | 0.0012        | -               |
+| 1.0833 | 1300 | 0.0006        | -               |
+| 1.125  | 1350 | 0.0005        | -               |
+| 1.1667 | 1400 | 0.0003        | -               |
+| 1.2083 | 1450 | 0.0012        | -               |
+| 1.25   | 1500 | 0.0006        | -               |
+| 1.2917 | 1550 | 0.0008        | -               |
+| 1.3333 | 1600 | 0.0008        | -               |
+| 1.375  | 1650 | 0.0003        | -               |
+| 1.4167 | 1700 | 0.0004        | -               |
+| 1.4583 | 1750 | 0.0005        | -               |
+| 1.5    | 1800 | 0.0004        | -               |
+| 1.5417 | 1850 | 0.0004        | -               |
+| 1.5833 | 1900 | 0.0008        | -               |
+| 1.625  | 1950 | 0.0004        | -               |
+| 1.6667 | 2000 | 0.0004        | -               |
+| 1.7083 | 2050 | 0.0021        | -               |
+| 1.75   | 2100 | 0.0004        | -               |
+| 1.7917 | 2150 | 0.0002        | -               |
+| 1.8333 | 2200 | 0.0006        | -               |
+| 1.875  | 2250 | 0.0004        | -               |
+| 1.9167 | 2300 | 0.0006        | -               |
+| 1.9583 | 2350 | 0.0006        | -               |
+| 2.0    | 2400 | 0.0003        | -               |
+| 2.0417 | 2450 | 0.0002        | -               |
+| 2.0833 | 2500 | 0.0002        | -               |
+| 2.125  | 2550 | 0.0003        | -               |
+| 2.1667 | 2600 | 0.0004        | -               |
+| 2.2083 | 2650 | 0.0004        | -               |
+| 2.25   | 2700 | 0.0005        | -               |
+| 2.2917 | 2750 | 0.0005        | -               |
+| 2.3333 | 2800 | 0.0005        | -               |
+| 2.375  | 2850 | 0.0007        | -               |
+| 2.4167 | 2900 | 0.0002        | -               |
+| 2.4583 | 2950 | 0.0003        | -               |
+| 2.5    | 3000 | 0.0004        | -               |
+| 2.5417 | 3050 | 0.0002        | -               |
+| 2.5833 | 3100 | 0.0004        | -               |
+| 2.625  | 3150 | 0.0002        | -               |
+| 2.6667 | 3200 | 0.0002        | -               |
+| 2.7083 | 3250 | 0.0003        | -               |
+| 2.75   | 3300 | 0.0002        | -               |
+| 2.7917 | 3350 | 0.0002        | -               |
+| 2.8333 | 3400 | 0.0003        | -               |
+| 2.875  | 3450 | 0.0002        | -               |
+| 2.9167 | 3500 | 0.0002        | -               |
+| 2.9583 | 3550 | 0.0002        | -               |
+| 3.0    | 3600 | 0.0002        | -               |
+
+### Framework Versions
+- Python: 3.10.13
+- SetFit: 1.0.1
+- Sentence Transformers: 2.2.2
+- Transformers: 4.35.0
+- PyTorch: 2.1.0
+- Datasets: 2.14.6
+- Tokenizers: 0.14.1
+
+## 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": "/Users/ali/.cache/torch/sentence_transformers/sentence-transformers_paraphrase-mpnet-base-v2/",
+  "architectures": [
+    "MPNetModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "mpnet",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "relative_attention_num_buckets": 32,
+  "torch_dtype": "float32",
+  "transformers_version": "4.35.0",
+  "vocab_size": 30527
+}

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

model.safetensors

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+size 437967672

model_head.pkl

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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"
+  }
+]

sentence_bert_config.json

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

special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
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+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
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+    "lstrip": false,
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+    "rstrip": false,
+    "single_word": false
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+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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+    "content": "<pad>",
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+    "normalized": false,
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+  "sep_token": {
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+    "lstrip": false,
+    "normalized": true,
+    "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": {
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+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
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+      "special": true
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+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "eos_token": "</s>",
+  "mask_token": "<mask>",
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "MPNetTokenizer",
+  "unk_token": "[UNK]"
+}