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README.md

@@ -1,189 +1,130 @@
 ---
-language:
-- en
-license: apache-2.0
-library_name: setfit
 tags:
 - setfit
 - sentence-transformers
 - text-classification
-- sentiment-analysis
-- few-shot-learning
-pipeline_tag: text-classification
+- generated_from_setfit_trainer
+widget: []
 metrics:
 - accuracy
-- f1
-- precision
-- recall
-model-index:
-- name: SetFit Sentiment Analysis
-  results:
-  - task:
-      type: text-classification
-      name: Sentiment Analysis
-    metrics:
-    - name: Accuracy
-      type: accuracy
-      value: 0.9
-    - name: F1 (Weighted)
-      type: f1
-      value: 0.8984430773904458
-    - name: Precision (Weighted)
-      type: precision
-      value: 0.9060606060606061
-    - name: Recall (Weighted)
-      type: recall
-      value: 0.9
+pipeline_tag: text-classification
+library_name: setfit
+inference: true
 ---
 
-# SetFit Sentiment Analysis Model
-
-This is a [SetFit](https://github.com/huggingface/setfit) model fine-tuned for sentiment classification on customer feedback data.
-
-## Model Description
-
-| Property | Value |
-|----------|-------|
-| **Base Model** | [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) |
-| **Total Parameters** | 109,482,240 |
-| **Trainable Parameters** | 109,482,240 |
-| **Body Parameters** | 109,482,240 |
-| **Head Parameters** | 0 |
-| **Model Size** | 417.64 MB |
-| **Labels** | [0, 1, 2, 3, 4] |
-| **Number of Classes** | 5 |
-| **Serialization** | safetensors |
-
-## Training Configuration
-
-| Parameter | Value |
-|-----------|-------|
-| **Batch Size** | 4 |
-| **Epochs** | [1, 16] |
-| **Training Samples** | 540 |
-| **Test Samples** | 100 |
-| **Loss Function** | CosineSimilarityLoss |
-| **Metric for Best Model** | embedding_loss |
-
-### Training Progress
-
-- **Initial Loss:** 0.1474
-- **Final Loss:** 0.0648
-- **Eval Loss:** 0.0918
-- **Training Runtime:** 2943.9747 seconds
-- **Samples/Second:** 3.6690
-
-## Evaluation Results
-
-| Metric | Score |
-|--------|-------|
-| **Accuracy** | 0.9000 |
-| **F1 (Weighted)** | 0.8984 |
-| **F1 (Macro)** | 0.8984 |
-| **Precision (Weighted)** | 0.9061 |
-| **Precision (Macro)** | 0.9061 |
-| **Recall (Weighted)** | 0.9000 |
-| **Recall (Macro)** | 0.9000 |
-
-### Per-Class Performance
+# SetFit
 
-```
-              precision    recall  f1-score   support
+This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
 
-           0       0.86      0.95      0.90        20
-           1       0.83      0.75      0.79        20
-           2       0.83      1.00      0.91        20
-           3       1.00      0.80      0.89        20
-           4       1.00      1.00      1.00        20
+The model has been trained using an efficient few-shot learning technique that involves:
 
-    accuracy                           0.90       100
-   macro avg       0.91      0.90      0.90       100
-weighted avg       0.91      0.90      0.90       100
+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:** [Unknown](https://huggingface.co/unknown) -->
+- **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:** 5 classes
+<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
 
-## Visualizations
+### Model Sources
 
-### Evaluation Metrics Overview
-<p align="center">
-  <img src="evaluation_metrics.png" alt="Evaluation Metrics" width="800"/>
-</p>
+- **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)
 
-### Confusion Matrix
-<p align="center">
-  <img src="confusion_matrix.png" alt="Confusion Matrix" width="600"/>
-</p>
+## Uses
 
-### Training Loss Curve
-<p align="center">
-  <img src="loss_curve.png" alt="Training Loss Curve" width="600"/>
-</p>
+### Direct Use for Inference
 
-### Learning Rate Schedule
-<p align="center">
-  <img src="learning_rate.png" alt="Learning Rate Schedule" width="600"/>
-</p>
+First install the SetFit library:
 
-## Usage
+```bash
+pip install setfit
+```
+
+Then you can load this model and run inference.
 
 ```python
 from setfit import SetFitModel
 
-# Load the model
-model = SetFitModel.from_pretrained("loganh274/nlp-testing-setfit")
-
-# Single prediction
-text = "This product exceeded my expectations!"
-prediction = model.predict([text])
-print(f"Sentiment: {prediction[0]}")
-
-# Batch prediction
-texts = [
-    "Amazing quality, highly recommend!",
-    "It's okay, nothing special.",
-    "Terrible experience, very disappointed.",
-]
-predictions = model.predict(texts)
-probabilities = model.predict_proba(texts)
-
-for text, pred, prob in zip(texts, predictions, probabilities):
-    print(f"Text: {text}")
-    print(f"  Prediction: {pred}, Confidence: {max(prob):.2%}")
+# Download from the 🤗 Hub
+model = SetFitModel.from_pretrained("setfit_model_id")
+# Run inference
+preds = model("I loved the spiderman movie!")
 ```
 
-## Label Mapping
+<!--
+### Downstream Use
 
-| Label | Sentiment |
-|-------|-----------|
-| 0 | Negative |
-| 1 | Somewhat Negative |
-| 2 | Neutral |
-| 3 | Somewhat Positive |
-| 4 | Positive |
+*List how someone could finetune this model on their own dataset.*
+-->
 
-## Environment
+<!--
+### Out-of-Scope Use
 
-| Package | Version |
-|---------|---------|
-| Python | 3.11.14 |
-| SetFit | 1.1.3 |
-| PyTorch | 2.9.1 |
-| scikit-learn | 1.8.0 |
-| Transformers | N/A |
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
 
-## Citation
+<!--
+## Bias, Risks and Limitations
 
-If you use this model, please cite the SetFit paper:
+*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
+
+### Framework Versions
+- Python: 3.11.14
+- SetFit: 1.1.3
+- Sentence Transformers: 5.2.0
+- Transformers: 4.57.5
+- PyTorch: 2.9.1
+- Datasets: 4.5.0
+- Tokenizers: 0.22.2
+
+## Citation
+
+### BibTeX
 ```bibtex
-@article{tunstall2022efficient,
-  title={Efficient Few-Shot Learning Without Prompts},
-  author={Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
-  journal={arXiv preprint arXiv:2209.11055},
-  year={2022}
+@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}
 }
 ```
 
-## License
+<!--
+## 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
 
-Apache 2.0
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

model.safetensors

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model_head.pkl

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