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Push model using huggingface_hub.

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  1. README.md +5 -6
  2. model.safetensors +1 -1
  3. model_head.pkl +1 -1
README.md CHANGED
@@ -97,12 +97,11 @@ metrics:
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  pipeline_tag: text-classification
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  library_name: setfit
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  inference: true
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- base_model: TaylorAI/bge-micro-v2
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  ---
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- # SetFit with TaylorAI/bge-micro-v2
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- 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.
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  The model has been trained using an efficient few-shot learning technique that involves:
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@@ -113,7 +112,7 @@ The model has been trained using an efficient few-shot learning technique that i
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  ### Model Description
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  - **Model Type:** SetFit
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- - **Sentence Transformer body:** [TaylorAI/bge-micro-v2](https://huggingface.co/TaylorAI/bge-micro-v2)
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  - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
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  - **Maximum Sequence Length:** 512 tokens
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  - **Number of Classes:** 2 classes
@@ -187,8 +186,8 @@ preds = model("I guess I was attracted to this film both because of the sound of
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  | Label | Training Sample Count |
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  |:---------|:----------------------|
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- | negative | 8 |
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  | positive | 8 |
 
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  ### Training Hyperparameters
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  - batch_size: (16, 2)
@@ -211,7 +210,7 @@ preds = model("I guess I was attracted to this film both because of the sound of
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  ### Training Results
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  | Epoch | Step | Training Loss | Validation Loss |
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  |:------:|:----:|:-------------:|:---------------:|
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- | 0.1111 | 1 | 0.1635 | - |
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  ### Framework Versions
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  - Python: 3.11.13
 
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  pipeline_tag: text-classification
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  library_name: setfit
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  inference: true
 
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  ---
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+ # SetFit
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+ 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.
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  The model has been trained using an efficient few-shot learning technique that involves:
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  ### Model Description
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  - **Model Type:** SetFit
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+ <!-- - **Sentence Transformer:** [Unknown](https://huggingface.co/unknown) -->
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  - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
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  - **Maximum Sequence Length:** 512 tokens
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  - **Number of Classes:** 2 classes
 
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  | Label | Training Sample Count |
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  |:---------|:----------------------|
 
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  | positive | 8 |
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+ | negative | 8 |
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  ### Training Hyperparameters
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  - batch_size: (16, 2)
 
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  ### Training Results
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  | Epoch | Step | Training Loss | Validation Loss |
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  |:------:|:----:|:-------------:|:---------------:|
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+ | 0.1111 | 1 | 0.1572 | - |
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  ### Framework Versions
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  - Python: 3.11.13
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