Push model using huggingface_hub.

README.md

@@ -4,17 +4,23 @@ tags:
 - sentence-transformers
 - text-classification
 - generated_from_setfit_trainer
-widget: []
+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
 metrics:
 - accuracy
 pipeline_tag: text-classification
 library_name: setfit
 inference: true
+base_model: TaylorAI/bge-micro-v2
 ---
 
-# SetFit
+# SetFit with TaylorAI/bge-micro-v2
 
-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.
+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.
 
 The model has been trained using an efficient few-shot learning technique that involves:
 
@@ -25,7 +31,7 @@ The model has been trained using an efficient few-shot learning technique that i
 
 ### Model Description
 - **Model Type:** SetFit
-<!-- - **Sentence Transformer:** [Unknown](https://huggingface.co/unknown) -->
+- **Sentence Transformer body:** [TaylorAI/bge-micro-v2](https://huggingface.co/TaylorAI/bge-micro-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
@@ -39,6 +45,12 @@ 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
@@ -57,7 +69,7 @@ from setfit import SetFitModel
 # Download from the 🤗 Hub
 model = SetFitModel.from_pretrained("mahitha-t/text_classification_model")
 # Run inference
-preds = model("I loved the spiderman movie!")
+preds = model("This sentence is positive")
 ```
 
 <!--
@@ -86,6 +98,39 @@ preds = model("I loved the spiderman movie!")
 
 ## 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

model.safetensors

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model_head.pkl

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