Add SetFit model

README.md

@@ -7,10 +7,40 @@ tags:
 - generated_from_setfit_trainer
 metrics:
 - accuracy
-widget: []
+- precision
+- recall
+- f1
+widget:
+- text: 'Recognized as one of the Most Energy Efficient Dealerships in North America! '
+- text: 'Nespresso VertuoLine WS Keurig 2.0 '
+- text: Threat Intelligence & Brand Reputation
+- text: 'FpeANUTOUTTER- CUPS '
 pipeline_tag: text-classification
 inference: true
 base_model: sentence-transformers/paraphrase-mpnet-base-v2
+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
+    - type: precision
+      value: 1.0
+      name: Precision
+    - type: recall
+      value: 1.0
+      name: Recall
+    - type: f1
+      value: 1.0
+      name: F1
 ---
 
 # SetFit with sentence-transformers/paraphrase-mpnet-base-v2
@@ -29,7 +59,7 @@ The model has been trained using an efficient few-shot learning technique that i
 - **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:** Unknown -->
+- **Number of Classes:** 2 classes
 <!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
 <!-- - **Language:** Unknown -->
 <!-- - **License:** Unknown -->
@@ -40,6 +70,19 @@ 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                                                                                                                                                                                                                                                                                                                                                                                                          |
+|:------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| True  | <ul><li>'Solve lunch first. Introducing The 12™, made with seasoned Canadian chicken breast, fresh tomato and crisp lettuce. '</li><li>'MAKE THE MOST OF _ Notional Ube Chocoleté Diy '</li><li>'ee ee Ra bere car 100% nisared } Bon ard whist riekes is wo ire. Ta fag eesti eas nen Pa asered, hoathy and hing groen with every sip of  Potand Spring? Grand 100% Natural Spring Warler you eqioy. '</li></ul> |
+| False | <ul><li>'Wykorzystywanie ograniczonych danych do wyboru treści '</li><li>'GitHub'</li><li>'Draftsmen'</li></ul>                                                                                                                                                                                                                                                                                                   |
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy | Precision | Recall | F1  |
+|:--------|:---------|:----------|:-------|:----|
+| **all** | 1.0      | 1.0       | 1.0    | 1.0 |
+
 ## Uses
 
 ### Direct Use for Inference
@@ -58,7 +101,7 @@ from setfit import SetFitModel
 # Download from the 🤗 Hub
 model = SetFitModel.from_pretrained("setfit_model_id")
 # Run inference
-preds = model("I loved the spiderman movie!")
+preds = model("FpeANUTOUTTER- CUPS ")
 ```
 
 <!--
@@ -87,6 +130,40 @@ preds = model("I loved the spiderman movie!")
 
 ## Training Details
 
+### Training Set Metrics
+| Training set | Min | Median | Max |
+|:-------------|:----|:-------|:----|
+| Word count   | 1   | 7.5625 | 41  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| False | 9                     |
+| True  | 7                     |
+
+### Training Hyperparameters
+- batch_size: (16, 2)
+- num_epochs: (1, 16)
+- max_steps: -1
+- sampling_strategy: oversampling
+- num_iterations: 20
+- 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
+- seed: 42
+- run_name: PG-OCR-test-3
+- eval_max_steps: -1
+- load_best_model_at_end: False
+
+### Training Results
+| Epoch | Step | Training Loss | Validation Loss |
+|:-----:|:----:|:-------------:|:---------------:|
+| 0.025 | 1    | 0.027         | -               |
+
 ### Framework Versions
 - Python: 3.11.0
 - SetFit: 1.0.3

config.json

@@ -1,5 +1,5 @@
 {
-  "_name_or_path": ".\\checkpoints\\step_40000",
+  "_name_or_path": "./checkpoints/step_40000",
   "architectures": [
     "MPNetModel"
   ],

config_setfit.json

@@ -1,4 +1,4 @@
 {
-  "normalize_embeddings": false,
-  "labels": null
+  "labels": null,
+  "normalize_embeddings": false
 }

model.safetensors

@@ -1,3 +1,3 @@
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model_head.pkl

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