Add SetFit model

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

1_Pooling/config.json

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+{
+  "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,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

README.md

@@ -0,0 +1,190 @@
+---
+library_name: setfit
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+metrics:
+- accuracy
+widget:
+- text: delivery and food preparation was suoer fast. nice
+- text: Hard, stale cookies that were probably sitting out for days.
+- text: I ordered an extra side of guacamole that never arrived with my meal.
+- text: Kulang talaga ang mga sangkap, mukhang hindi kumpleto ang aking order.
+- text: The steak was so overcooked and tough, I couldn't even cut through it with
+    a knife.
+pipeline_tag: text-classification
+inference: true
+base_model: meedan/paraphrase-filipino-mpnet-base-v2
+---
+
+# SetFit with meedan/paraphrase-filipino-mpnet-base-v2
+
+This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [meedan/paraphrase-filipino-mpnet-base-v2](https://huggingface.co/meedan/paraphrase-filipino-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:** [meedan/paraphrase-filipino-mpnet-base-v2](https://huggingface.co/meedan/paraphrase-filipino-mpnet-base-v2)
+- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
+- **Maximum Sequence Length:** 128 tokens
+- **Number of Classes:** 6 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                                                                                                                                                                                                                                                                                            |
+|:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 0     | <ul><li>'I specifically asked for no onions, yet my sandwich was loaded with them when delivered.'</li><li>'The delivery driver spilled half my order all over the bag. What a mess!'</li><li>'Two hour wait only for my pizza to arrive burnt on the bottom from sitting too long.'</li></ul>      |
+| 2     | <ul><li>'Found a long strand of hair hanging out of my sealed takeout burger container.'</li><li>'Bits of plastic were baked into the crust of the takeout pizza I received.'</li><li>'The takeout container for my soup was leaking and left a trail of foul-smelling liquid.'</li></ul>           |
+| 1     | <ul><li>'Sobrang luto at tigas na para bang kahoy ang aking karne.'</li><li>'Sobrang lata ng pagkaluto, hindi na makain ang aking litsong manok.'</li><li>'Pizza crust was burnt black on the bottom yet still doughy raw on top.'</li></ul>                                                        |
+| 3     | <ul><li>'Half the ingredients were missing from my order like they forgot to include them.'</li><li>'Binayaran ko ang dami, pero napakaliit lang ng portion size na naibigay sa akin.'</li><li>'The plate looked full but it was all rice, with small paltry portions of the main items.'</li></ul> |
+| 4     | <ul><li>'Bland, overcooked chicken, soggy vegetables and hard, stale naan bread.'</li><li>'Tiny portion sizes, freezing cold plates, and a hair baked into the bread.'</li><li>'Every single thing I tried to order was met with confusion, attitude and mistakes.'</li></ul>                       |
+| 5     | <ul><li>'From the appetizer to dessert, everything was prepared flawlessly. 10/10!'</li><li>"The chilaquiles were authentic, flavor-packed and easily the best I've had."</li><li>'You can really taste the freshness of the local ingredients in every bite.'</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("bsen26/eyeR-classification-model-1.0")
+# Run inference
+preds = model("delivery and food preparation was suoer fast. nice")
+```
+
+<!--
+### 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   | 12.6833 | 17  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0     | 20                    |
+| 1     | 20                    |
+| 2     | 20                    |
+| 3     | 20                    |
+| 4     | 20                    |
+| 5     | 20                    |
+
+### 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 |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0033 | 1    | 0.2048        | -               |
+| 0.1667 | 50   | 0.048         | -               |
+| 0.3333 | 100  | 0.0148        | -               |
+| 0.5    | 150  | 0.0011        | -               |
+| 0.6667 | 200  | 0.0009        | -               |
+| 0.8333 | 250  | 0.0005        | -               |
+| 1.0    | 300  | 0.0008        | -               |
+
+### Framework Versions
+- Python: 3.10.12
+- SetFit: 1.0.3
+- Sentence Transformers: 2.6.1
+- Transformers: 4.38.2
+- PyTorch: 2.2.1+cu121
+- Datasets: 2.18.0
+- Tokenizers: 0.15.2
+
+## 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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+{
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+  "num_hidden_layers": 12,
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+  "type_vocab_size": 1,
+  "use_cache": true,
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+}

config_sentence_transformers.json

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+    "sentence_transformers": "2.2.0",
+    "transformers": "4.17.0",
+    "pytorch": "1.10.0"
+  },
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+}

config_setfit.json

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+  "normalize_embeddings": false
+}

model.safetensors

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modules.json

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+    "type": "sentence_transformers.models.Pooling"
+  }
+]

sentence_bert_config.json

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+  "max_seq_length": 128,
+  "do_lower_case": false
+}

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special_tokens_map.json

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tokenizer_config.json

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