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README.md

@@ -1,195 +1,30 @@
 ---
-library_name: setfit
-tags:
-- setfit
-- sentence-transformers
-- text-classification
-- generated_from_setfit_trainer
-base_model: sentence-transformers/paraphrase-mpnet-base-v2
-metrics:
-- accuracy
-widget:
-- text: What happens if I drive with low tire pressure?
-- text: How often should I rotate my tires?
-- text: How can I tell if my tire is properly balanced?
-- text: How does tire pressure affect handling and braking?
-pipeline_tag: text-classification
-inference: true
-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: 1.0
-      name: Accuracy
+language: en
+license: apache-2.0
 ---
 
-# SetFit with sentence-transformers/paraphrase-mpnet-base-v2
+# phospho-small
 
-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.
+This is a SetFit model that can be used for Text Classification on CPU.
 
-The model has been trained using an efficient few-shot learning technique that involves:
+The model has been trained using an efficient few-shot learning technique.
 
-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.
+## Usage
 
-## Model Details
+ ```python
+ from setfit import SetFitModel
 
-### 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:** 2 classes
-<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
-<!-- - **Language:** Unknown -->
-<!-- - **License:** Unknown -->
+model = SetFitModel.from_pretrained("phospho-small-2502093")
 
-### 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                                                                                                                                                                                                 |
-|:------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| True  | <ul><li>'Can I use nitrogen instead of air to inflate my tires?'</li><li>'What should I do if my tire pressure warning light comes on?'</li><li>'Is it okay to slightly overinflate my tires?'</li></ul> |
-| False | <ul><li>'Can I mix different brands of tires on my vehicle?'</li><li>'How do I know if my tire has a slow leak?'</li><li>'How can I extend the life of my tires?'</li></ul>                              |
-
-## Evaluation
-
-### Metrics
-| Label   | Accuracy |
-|:--------|:---------|
-| **all** | 1.0      |
-
-## 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("How often should I rotate my tires?")
-```
-
-<!--
-### 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   | 7   | 10.25  | 13  |
-
-| Label | Training Sample Count |
-|:------|:----------------------|
-| False | 7                     |
-| True  | 9                     |
-
-### 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 |
-|:-----:|:----:|:-------------:|:---------------:|
-| 0.025 | 1    | 0.1742        | -               |
-
-### Framework Versions
-- Python: 3.11.6
-- SetFit: 1.0.3
-- Sentence Transformers: 2.7.0
-- Transformers: 4.40.1
-- PyTorch: 2.3.0
-- 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}
-}
+outputs = model.predict(["This is a sentence to classify", "Another sentence"])
+# tensor([1, 0])
 ```
 
-<!--
-## Glossary
-
-*Clearly define terms in order to be accessible across audiences.*
--->
+## References
 
-<!--
-## Model Card Authors
+This work was possible thanks to the SetFit library and the work of:
 
-*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
--->
+Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren (2022). Efficient Few-Shot Learning Without Prompts. 
 
-<!--
-## Model Card Contact
+ArXiv: [https://doi.org/10.48550/arxiv.2209.11055](https://doi.org/10.48550/arxiv.2209.11055)
 
-*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
--->