Push model using huggingface_hub.

README.md

@@ -4,23 +4,17 @@ tags:
 - sentence-transformers
 - text-classification
 - generated_from_setfit_trainer
-widget:
-- text: This sentence is positive
-- text: This sentence is positive
-- text: This sentence is negative
-- text: This sentence is positive
-- text: This sentence is negative
+widget: []
 metrics:
 - accuracy
 pipeline_tag: text-classification
 library_name: setfit
 inference: true
-base_model: TaylorAI/bge-micro-v2
 ---
 
-# SetFit with TaylorAI/bge-micro-v2
+# SetFit
 
-This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [TaylorAI/bge-micro-v2](https://huggingface.co/TaylorAI/bge-micro-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](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.
 
 The model has been trained using an efficient few-shot learning technique that involves:
 
@@ -31,7 +25,7 @@ The model has been trained using an efficient few-shot learning technique that i
 
 ### Model Description
 - **Model Type:** SetFit
-- **Sentence Transformer body:** [TaylorAI/bge-micro-v2](https://huggingface.co/TaylorAI/bge-micro-v2)
+<!-- - **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:** 2 classes
@@ -45,12 +39,6 @@ The model has been trained using an efficient few-shot learning technique that i
 - **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>'This sentence is positive'</li><li>'This sentence is positive'</li><li>'This sentence is positive'</li></ul> |
-| 1     | <ul><li>'This sentence is negative'</li><li>'This sentence is negative'</li><li>'This sentence is negative'</li></ul> |
-
 ## Uses
 
 ### Direct Use for Inference
@@ -69,7 +57,7 @@ from setfit import SetFitModel
 # Download from the 🤗 Hub
 model = SetFitModel.from_pretrained("mahitha-t/text_classification_model")
 # Run inference
-preds = model("This sentence is positive")
+preds = model("I loved the spiderman movie!")
 ```
 
 <!--
@@ -98,39 +86,6 @@ preds = model("This sentence is positive")
 
 ## Training Details
 
-### Training Set Metrics
-| Training set | Min | Median | Max |
-|:-------------|:----|:-------|:----|
-| Word count   | 4   | 4.0    | 4   |
-
-| Label | Training Sample Count |
-|:------|:----------------------|
-| 0     | 8                     |
-| 1     | 8                     |
-
-### Training Hyperparameters
-- batch_size: (16, 2)
-- num_epochs: (1, 16)
-- 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
-- l2_weight: 0.01
-- seed: 42
-- eval_max_steps: -1
-- load_best_model_at_end: False
-
-### Training Results
-| Epoch  | Step | Training Loss | Validation Loss |
-|:------:|:----:|:-------------:|:---------------:|
-| 0.1111 | 1    | 0.1561        | -               |
-
 ### Framework Versions
 - Python: 3.11.13
 - SetFit: 1.1.2