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1_Pooling/config.json

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+{
+  "word_embedding_dimension": 512,
+  "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
+base_model: google-t5/t5-small
+metrics:
+- accuracy
+widget:
+- text: Do you have any special deals or discounts on bulk items?
+- text: I'd like to exchange a product I bought in-store. Do I need to bring the original
+    receipt?
+- text: I have a question about freight shipping rates for a bulk order I'm considering
+    placing
+- text: I need to find some dairy-free milk alternatives. What options do you carry?
+- text: I purchased a product that was supposed to be on sale but I didn't get the
+    discounted price. Can I get a credit for the difference?
+pipeline_tag: text-classification
+inference: true
+---
+
+# SetFit with google-t5/t5-small
+
+This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [google-t5/t5-small](https://huggingface.co/google-t5/t5-small) 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:** [google-t5/t5-small](https://huggingface.co/google-t5/t5-small)
+- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
+- **Maximum Sequence Length:** None tokens
+- **Number of Classes:** 5 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                                                                                                                                                                                                                                                                                                                                                             |
+|:-------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| Tech Support | <ul><li>"My loyalty card isn't working at the checkout. What should I do?"</li><li>'How can I reset my password for the online account?'</li><li>'How can I reset my password for the online account?'</li></ul>                                                                                                                                                     |
+| HR           | <ul><li>"I'm interested in applying for a job at your company. Can you provide information on current openings?"</li><li>'I have a question about my paycheck. Who should I contact?'</li><li>"I'm having an issue with my timesheet submission. Who should I contact?"</li></ul>                                                                                    |
+| Product      | <ul><li>'What brand of nut butters do you carry that are peanut-free?'</li><li>'Do you offer any delivery or pickup options for online grocery orders?'</li><li>'I have a dietary restriction - how can I easily identify suitable products?'</li></ul>                                                                                                              |
+| Returns      | <ul><li>'My grocery delivery contained items that were spoiled or past their expiration date. How do I get replacements?'</li><li>"I purchased a product that was supposed to be on sale but I didn't get the discounted price. Can I get a credit for the difference?"</li><li>"I bought an item that doesn't fit. What's the process for exchanging it?"</li></ul> |
+| Logistics    | <ul><li>'My delivery was marked as "undeliverable" - what are the next steps I should take?'</li><li>'I need to change the delivery address for my upcoming order. How can I do that?'</li><li>'Is there a way to get real-time updates on the status of my order during the shipping process?'</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("setfit_model_id")
+# Run inference
+preds = model("Do you have any special deals or discounts on bulk items?")
+```
+
+<!--
+### 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   | 10  | 14.25  | 26  |
+
+| Label        | Training Sample Count |
+|:-------------|:----------------------|
+| Returns      | 8                     |
+| Tech Support | 8                     |
+| Logistics    | 8                     |
+| HR           | 8                     |
+| Product      | 8                     |
+
+### Training Hyperparameters
+- batch_size: (32, 32)
+- num_epochs: (100, 100)
+- max_steps: -1
+- sampling_strategy: oversampling
+- 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 |
+|:-----:|:----:|:-------------:|:---------------:|
+| 0.025 | 1    | 0.2674        | -               |
+| 1.25  | 50   | 0.2345        | -               |
+| 2.5   | 100  | 0.2558        | -               |
+| 3.75  | 150  | 0.2126        | -               |
+| 5.0   | 200  | 0.1904        | -               |
+| 6.25  | 250  | 0.1965        | -               |
+| 7.5   | 300  | 0.2013        | -               |
+| 8.75  | 350  | 0.1221        | -               |
+| 10.0  | 400  | 0.1254        | -               |
+| 11.25 | 450  | 0.0791        | -               |
+| 12.5  | 500  | 0.0917        | -               |
+| 13.75 | 550  | 0.0757        | -               |
+| 15.0  | 600  | 0.0446        | -               |
+| 16.25 | 650  | 0.0407        | -               |
+| 17.5  | 700  | 0.0276        | -               |
+| 18.75 | 750  | 0.0297        | -               |
+| 20.0  | 800  | 0.017         | -               |
+| 21.25 | 850  | 0.0193        | -               |
+| 22.5  | 900  | 0.0105        | -               |
+| 23.75 | 950  | 0.0143        | -               |
+| 25.0  | 1000 | 0.0133        | -               |
+| 26.25 | 1050 | 0.0127        | -               |
+| 27.5  | 1100 | 0.0064        | -               |
+| 28.75 | 1150 | 0.0076        | -               |
+| 30.0  | 1200 | 0.0099        | -               |
+| 31.25 | 1250 | 0.0077        | -               |
+| 32.5  | 1300 | 0.0059        | -               |
+| 33.75 | 1350 | 0.0047        | -               |
+| 35.0  | 1400 | 0.0059        | -               |
+| 36.25 | 1450 | 0.005         | -               |
+| 37.5  | 1500 | 0.005         | -               |
+| 38.75 | 1550 | 0.005         | -               |
+| 40.0  | 1600 | 0.0043        | -               |
+| 41.25 | 1650 | 0.0056        | -               |
+| 42.5  | 1700 | 0.0036        | -               |
+| 43.75 | 1750 | 0.0029        | -               |
+| 45.0  | 1800 | 0.0031        | -               |
+| 46.25 | 1850 | 0.0033        | -               |
+| 47.5  | 1900 | 0.0028        | -               |
+| 48.75 | 1950 | 0.0042        | -               |
+| 50.0  | 2000 | 0.0038        | -               |
+| 51.25 | 2050 | 0.0032        | -               |
+| 52.5  | 2100 | 0.0033        | -               |
+| 53.75 | 2150 | 0.0031        | -               |
+| 55.0  | 2200 | 0.0023        | -               |
+| 56.25 | 2250 | 0.002         | -               |
+| 57.5  | 2300 | 0.003         | -               |
+| 58.75 | 2350 | 0.0039        | -               |
+| 60.0  | 2400 | 0.003         | -               |
+| 61.25 | 2450 | 0.0035        | -               |
+| 62.5  | 2500 | 0.0022        | -               |
+| 63.75 | 2550 | 0.0029        | -               |
+| 65.0  | 2600 | 0.0029        | -               |
+| 66.25 | 2650 | 0.0019        | -               |
+| 67.5  | 2700 | 0.002         | -               |
+| 68.75 | 2750 | 0.0041        | -               |
+| 70.0  | 2800 | 0.0022        | -               |
+| 71.25 | 2850 | 0.0027        | -               |
+| 72.5  | 2900 | 0.0016        | -               |
+| 73.75 | 2950 | 0.002         | -               |
+| 75.0  | 3000 | 0.0029        | -               |
+| 76.25 | 3050 | 0.0024        | -               |
+| 77.5  | 3100 | 0.0017        | -               |
+| 78.75 | 3150 | 0.0017        | -               |
+| 80.0  | 3200 | 0.0025        | -               |
+| 81.25 | 3250 | 0.0023        | -               |
+| 82.5  | 3300 | 0.0018        | -               |
+| 83.75 | 3350 | 0.0021        | -               |
+| 85.0  | 3400 | 0.0016        | -               |
+| 86.25 | 3450 | 0.0021        | -               |
+| 87.5  | 3500 | 0.0018        | -               |
+| 88.75 | 3550 | 0.0014        | -               |
+| 90.0  | 3600 | 0.0014        | -               |
+| 91.25 | 3650 | 0.0026        | -               |
+| 92.5  | 3700 | 0.0012        | -               |
+| 93.75 | 3750 | 0.0031        | -               |
+| 95.0  | 3800 | 0.0025        | -               |
+| 96.25 | 3850 | 0.0014        | -               |
+| 97.5  | 3900 | 0.0012        | -               |
+| 98.75 | 3950 | 0.0025        | -               |
+| 100.0 | 4000 | 0.002         | -               |
+
+### Framework Versions
+- Python: 3.11.8
+- SetFit: 1.0.3
+- Sentence Transformers: 2.7.0
+- Transformers: 4.40.0
+- PyTorch: 2.2.2
+- 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": "google-t5/t5-small",
+  "architectures": [
+    "T5EncoderModel"
+  ],
+  "classifier_dropout": 0.0,
+  "d_ff": 2048,
+  "d_kv": 64,
+  "d_model": 512,
+  "decoder_start_token_id": 0,
+  "dense_act_fn": "relu",
+  "dropout_rate": 0.1,
+  "eos_token_id": 1,
+  "feed_forward_proj": "relu",
+  "initializer_factor": 1.0,
+  "is_encoder_decoder": true,
+  "is_gated_act": false,
+  "layer_norm_epsilon": 1e-06,
+  "model_type": "t5",
+  "n_positions": 512,
+  "num_decoder_layers": 6,
+  "num_heads": 8,
+  "num_layers": 6,
+  "output_past": true,
+  "pad_token_id": 0,
+  "relative_attention_max_distance": 128,
+  "relative_attention_num_buckets": 32,
+  "task_specific_params": {
+    "summarization": {
+      "early_stopping": true,
+      "length_penalty": 2.0,
+      "max_length": 200,
+      "min_length": 30,
+      "no_repeat_ngram_size": 3,
+      "num_beams": 4,
+      "prefix": "summarize: "
+    },
+    "translation_en_to_de": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to German: "
+    },
+    "translation_en_to_fr": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to French: "
+    },
+    "translation_en_to_ro": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to Romanian: "
+    }
+  },
+  "torch_dtype": "float32",
+  "transformers_version": "4.40.0",
+  "use_cache": true,
+  "vocab_size": 32128
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "2.7.0",
+    "transformers": "4.40.0",
+    "pytorch": "2.2.2"
+  },
+  "prompts": {},
+  "default_prompt_name": null
+}

config_setfit.json

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+{
+  "normalize_embeddings": false,
+  "labels": [
+    "Returns",
+    "Tech Support",
+    "Logistics",
+    "HR",
+    "Product"
+  ]
+}

model.safetensors

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model_head.pkl

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modules.json

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+  {
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+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
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+    "type": "sentence_transformers.models.Pooling"
+  }
+]

sentence_bert_config.json

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

special_tokens_map.json

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tokenizer_config.json

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