Update README.md

README.md

@@ -5,45 +5,37 @@ tags:
 - sentence-transformers
 - text-classification
 pipeline_tag: text-classification
+library_name: sentence-transformers
 ---
 
-# /var/folders/05/wb3jgwtj5v967cjkdcnz4t700000gn/T/tmpe2s9igdk/splore/navigational-vs-transactional-vs-informational-classifier
+# splore/navigational-vs-transactional-vs-informational-classifier
 
-This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
+Query classification model trained using Metaflow.
 
-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.
+## Query types
+1. Navigational
+2. Transactional
+3. Informational
 
 ## Usage
 
-To use this model for inference, first install the SetFit library:
+```python
+from setfit import SetFitModel
 
-```bash
-python -m pip install setfit
+model = SetFitModel.from_pretrained("splore/navigational-vs-transactional-vs-informational-classifier")
+predections = model.predict(["What type of query is this?"])
+probabilities = model.predict_proba(["Predictions with probabilities"])
 ```
 
-You can then run inference as follows:
+## Evaluation
 
-```python
-from setfit import SetFitModel
+Tests have only been done on a small dataset of 40 manually created queries.
 
-# Download from Hub and run inference
-model = SetFitModel.from_pretrained("/var/folders/05/wb3jgwtj5v967cjkdcnz4t700000gn/T/tmpe2s9igdk/splore/navigational-vs-transactional-vs-informational-classifier")
-# Run inference
-preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
-```
+|metric     |value|
+|-----------|-----|
+|accuracy   |0.942|
+|exact match|0.875|
 
-## BibTeX entry and citation info
-
-```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}
-}
-```
+![ROC graph](roc.png)
+
+![confusion matrix](confusion.png)