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Shankhdhar
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classifier_woog

Text Classification
setfit
Safetensors
sentence-transformers
mpnet
generated_from_setfit_trainer
Eval Results
Model card Files Community
Shankhdhar commited on
Commit
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1 Parent(s): d89c51b

Add SetFit model

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Files changed (4) hide show
  1. README.md +152 -69
  2. config_setfit.json +3 -2
  3. model.safetensors +1 -1
  4. model_head.pkl +2 -2
README.md CHANGED
@@ -9,14 +9,15 @@ base_model: sentence-transformers/paraphrase-mpnet-base-v2
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  metrics:
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  - accuracy
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  widget:
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- - text: Is the lavender round empty decorative acrylic box available in a smaller
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- size?
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- - text: I recently purchased the Unicorn Dream Silver Earring, but I am disappointed
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- to find that the quality does not match what was advertised. The silver seems
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- to tarnish much faster than expected. Can you address this issue?
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- - text: Can I get a refund for a necklace if it has a manufacturing defect?
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- - text: Do you offer weekend or holiday deliveries for orders?
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- - text: What apparel do you have from Nike?
 
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  pipeline_tag: text-classification
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  inference: true
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  model-index:
@@ -31,7 +32,7 @@ model-index:
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  split: test
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  metrics:
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  - type: accuracy
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- value: 0.949685534591195
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  name: Accuracy
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  ---
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@@ -51,7 +52,7 @@ The model has been trained using an efficient few-shot learning technique that i
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  - **Sentence Transformer body:** [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-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:** 5 classes
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  <!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
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  <!-- - **Language:** Unknown -->
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  <!-- - **License:** Unknown -->
@@ -63,20 +64,21 @@ The model has been trained using an efficient few-shot learning technique that i
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  - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
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65
  ### Model Labels
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- | Label | Examples |
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- |:------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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- | product discoverability | <ul><li>'Do you have Converse sneakers in different colors?'</li><li>'pink bakery boxes for gifting'</li><li>'Could you suggest some Earring options that go well with traditional outfits?'</li></ul> |
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- | order tracking | <ul><li>"Can I track the delivery status of my order using the store's customer service hotline?"</li><li>"I recently ordered the Pakhi Handcrafted Earring but I haven't received any shipping confirmation. Could you please update me on the status of my order?"</li><li>'What is the process for claiming a lost or damaged shipment?'</li></ul> |
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- | complaints | <ul><li>"The Blossom Vintage cocktail ring I received looks tarnished and doesn't match the quality depicted on the website."</li><li>"I recently purchased the Teddy's Heartbeat Gold Pendant and I'm disappointed to see that the pendant scratches very easily. Is there anything that can be done about this?"</li><li>'I recently bought the Green Floral Bangles with White Rhodium Polish and I have noticed that the polish is already coming off. This is not what I expected so soon after purchase.'</li></ul> |
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- | product policy | <ul><li>'Do you offer a satisfaction guarantee for sneakers purchased on clearance?'</li><li>'Are earrings eligible for exchange in case I receive a defective piece?'</li><li>'Do you offer any authenticity certificates for necklaces made with precious stones and metals?'</li></ul> |
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- | product faq | <ul><li>'Do the Nike Blazer Mid sacai Snow Beach run small or large'</li><li>'Are there any special discounts on the PVC chocolate boxes for bulk orders for wholesale orders for wholesale orders?'</li><li>'Can the huge glitter heart rigid box be used for storage purposes?'</li></ul> |
 
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  ## Evaluation
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  ### Metrics
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  | Label | Accuracy |
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  |:--------|:---------|
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- | **all** | 0.9497 |
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  ## Uses
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@@ -96,7 +98,7 @@ from setfit import SetFitModel
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  # Download from the 🤗 Hub
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  model = SetFitModel.from_pretrained("setfit_model_id")
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  # Run inference
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- preds = model("What apparel do you have from Nike?")
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  ```
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  <!--
@@ -126,17 +128,18 @@ preds = model("What apparel do you have from Nike?")
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  ## Training Details
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  ### Training Set Metrics
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- | Training set | Min | Median | Max |
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- |:-------------|:----|:-------|:----|
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- | Word count | 4 | 16.58 | 37 |
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  | Label | Training Sample Count |
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  |:------------------------|:----------------------|
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- | complaints | 20 |
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- | order tracking | 20 |
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- | product discoverability | 20 |
 
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  | product faq | 20 |
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- | product policy | 20 |
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  ### Training Hyperparameters
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  - batch_size: (16, 16)
@@ -156,49 +159,129 @@ preds = model("What apparel do you have from Nike?")
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  - load_best_model_at_end: True
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  ### Training Results
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- | Epoch | Step | Training Loss | Validation Loss |
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- |:-----:|:----:|:-------------:|:---------------:|
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- | 0.002 | 1 | 0.2231 | - |
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- | 0.1 | 50 | 0.1432 | - |
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- | 0.2 | 100 | 0.0347 | - |
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- | 0.3 | 150 | 0.0031 | - |
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- | 0.4 | 200 | 0.0011 | - |
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- | 0.5 | 250 | 0.0007 | - |
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- | 0.6 | 300 | 0.0005 | - |
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- | 0.7 | 350 | 0.0003 | - |
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- | 0.8 | 400 | 0.0003 | - |
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- | 0.9 | 450 | 0.0002 | - |
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- | 1.0 | 500 | 0.0003 | - |
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- | 1.1 | 550 | 0.0003 | - |
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- | 1.2 | 600 | 0.0002 | - |
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- | 1.3 | 650 | 0.0002 | - |
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- | 1.4 | 700 | 0.0002 | - |
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- | 1.5 | 750 | 0.0002 | - |
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- | 1.6 | 800 | 0.0002 | - |
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- | 1.7 | 850 | 0.0001 | - |
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- | 1.8 | 900 | 0.0001 | - |
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- | 1.9 | 950 | 0.0002 | - |
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- | 2.0 | 1000 | 0.0001 | - |
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- | 2.1 | 1050 | 0.0001 | - |
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- | 2.2 | 1100 | 0.0001 | - |
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- | 2.3 | 1150 | 0.0001 | - |
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- | 2.4 | 1200 | 0.0001 | - |
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- | 2.5 | 1250 | 0.0001 | - |
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- | 2.6 | 1300 | 0.0001 | - |
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- | 2.7 | 1350 | 0.0001 | - |
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- | 2.8 | 1400 | 0.0001 | - |
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- | 2.9 | 1450 | 0.0001 | - |
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- | 3.0 | 1500 | 0.0001 | - |
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- | 3.1 | 1550 | 0.0001 | - |
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- | 3.2 | 1600 | 0.0001 | - |
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- | 3.3 | 1650 | 0.0001 | - |
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- | 3.4 | 1700 | 0.0001 | - |
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- | 3.5 | 1750 | 0.0001 | - |
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- | 3.6 | 1800 | 0.0001 | - |
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- | 3.7 | 1850 | 0.0001 | - |
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- | 3.8 | 1900 | 0.0001 | - |
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- | 3.9 | 1950 | 0.0001 | - |
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- | 4.0 | 2000 | 0.0001 | - |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework Versions
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  - Python: 3.9.16
 
9
  metrics:
10
  - accuracy
11
  widget:
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+ - text: I recently ordered the Bella Silver Pendant, but I haven't received any update
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+ about the shipment. Can you provide me with the current status of my order?
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+ - text: What is the metal purity of the Eternal Swirl Rose Gold Hoop Earring, and
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+ does it come with a certificate of authenticity?
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+ - text: Can you suggest some minimalist necklaces from your 'Best Sellers - Minimalist'
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+ range?
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+ - text: I recently ordered the Pearly Round Earring but haven't received any shipping
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+ updates. Can you please provide me with the tracking information?
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+ - text: what are the colors available in air jordan 4
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  pipeline_tag: text-classification
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  inference: true
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  model-index:
 
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  split: test
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  metrics:
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  - type: accuracy
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+ value: 0.8762886597938144
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  name: Accuracy
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  ---
38
 
 
52
  - **Sentence Transformer body:** [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2)
53
  - **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:** 6 classes
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  <!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
57
  <!-- - **Language:** Unknown -->
58
  <!-- - **License:** Unknown -->
 
64
  - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
65
 
66
  ### Model Labels
67
+ | Label | Examples |
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+ |:------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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+ | product policy | <ul><li>'Are there any exceptions to the return policy for items that were purchased with a special offer promotion?'</li><li>'What is your policy on returning sneakers with added paint or dye?'</li><li>'Do you offer exchanges for items that were purchased with a special event celebration?'</li></ul> |
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+ | order tracking | <ul><li>"I recently placed an order for the Regalia Gold Ring but I haven't received any confirmation or tracking details. Could you please update me on the status of my order?"</li><li>'What is the process for rerouting a shipment to a different address?'</li><li>"I recently ordered a Three Crystal Proposal Ring but haven't received any shipping updates yet. Could you please provide me with the current status of my order?"</li></ul> |
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+ | complaints | <ul><li>"I recently bought the Golden Love Affair Pendant, but it seems to have tarnished very quickly. I'm not satisfied with the quality. What can you do about this?"</li><li>"I recently purchased the Three Crystal Proposal Ring, but I'm disappointed to find that one of the crystals is loose. Can you assist me with this issue?"</li><li>'I received my Kali- Handcrafted Earring today, but I found that one earring is slightly different from the other in design. Can you help me with this issue?'</li></ul> |
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+ | product faq | <ul><li>'What is the material used for making the All the Stars Pendant Set, and does it come with matching earrings?'</li><li>'What is the Bold and Beautiful Link Ring made of, and could you provide information on sizing and care instructions?'</li><li>'What is the material used for making the Sheer Heart Ring, and is it available in different sizes?'</li></ul> |
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+ | product discoveribility | <ul><li>"I'm interested in necklaces that have an adjustable length. What options do you have?"</li><li>'Do you have any charm bracelets available at your store?'</li><li>'Could you suggest some pendants that would go well with traditional attire?'</li></ul> |
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+ | product discoverability | <ul><li>'Types of bakery boxes available'</li><li>'adidas sneakers under 25k'</li><li>'show me 100 cookie boxes under $50'</li></ul> |
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76
  ## Evaluation
77
 
78
  ### Metrics
79
  | Label | Accuracy |
80
  |:--------|:---------|
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+ | **all** | 0.8763 |
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83
  ## Uses
84
 
 
98
  # Download from the 🤗 Hub
99
  model = SetFitModel.from_pretrained("setfit_model_id")
100
  # Run inference
101
+ preds = model("what are the colors available in air jordan 4")
102
  ```
103
 
104
  <!--
 
128
  ## Training Details
129
 
130
  ### Training Set Metrics
131
+ | Training set | Min | Median | Max |
132
+ |:-------------|:----|:--------|:----|
133
+ | Word count | 4 | 16.2235 | 36 |
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135
  | Label | Training Sample Count |
136
  |:------------------------|:----------------------|
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+ | complaints | 30 |
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+ | order tracking | 30 |
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+ | product discoverability | 30 |
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+ | product discoveribility | 30 |
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  | product faq | 20 |
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+ | product policy | 30 |
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144
  ### Training Hyperparameters
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  - batch_size: (16, 16)
 
159
  - load_best_model_at_end: True
160
 
161
  ### Training Results
162
+ | Epoch | Step | Training Loss | Validation Loss |
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+ |:------:|:----:|:-------------:|:---------------:|
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+ | 0.0007 | 1 | 0.1501 | - |
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+ | 0.0333 | 50 | 0.1693 | - |
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+ | 0.0667 | 100 | 0.0692 | - |
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+ | 0.1 | 150 | 0.0311 | - |
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+ | 0.1333 | 200 | 0.0182 | - |
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+ | 0.1667 | 250 | 0.0033 | - |
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+ | 0.2 | 300 | 0.0025 | - |
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+ | 0.2333 | 350 | 0.0013 | - |
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+ | 0.2667 | 400 | 0.0008 | - |
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+ | 0.3 | 450 | 0.0011 | - |
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+ | 0.3333 | 500 | 0.0005 | - |
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+ | 0.3667 | 550 | 0.0005 | - |
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+ | 0.4 | 600 | 0.0003 | - |
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+ | 0.4333 | 650 | 0.0002 | - |
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+ | 0.4667 | 700 | 0.0003 | - |
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+ | 0.5 | 750 | 0.0003 | - |
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+ | 0.5333 | 800 | 0.0004 | - |
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+ | 0.5667 | 850 | 0.0003 | - |
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+ | 0.6 | 900 | 0.0002 | - |
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+ | 0.6333 | 950 | 0.0001 | - |
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+ | 0.6667 | 1000 | 0.0001 | - |
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+ | 0.7 | 1050 | 0.0001 | - |
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+ | 0.7333 | 1100 | 0.0002 | - |
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+ | 0.7667 | 1150 | 0.0002 | - |
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+ | 0.8 | 1200 | 0.0001 | - |
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+ | 0.8333 | 1250 | 0.0001 | - |
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+ | 0.8667 | 1300 | 0.0001 | - |
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+ | 0.9 | 1350 | 0.0001 | - |
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+ | 0.9333 | 1400 | 0.0002 | - |
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+ | 0.9667 | 1450 | 0.0001 | - |
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+ | 1.0 | 1500 | 0.0002 | - |
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+ | 1.0333 | 1550 | 0.0001 | - |
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+ | 1.0667 | 1600 | 0.0001 | - |
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+ | 1.1 | 1650 | 0.0001 | - |
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+ | 1.1333 | 1700 | 0.0001 | - |
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+ | 1.1667 | 1750 | 0.0002 | - |
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+ | 1.2 | 1800 | 0.0001 | - |
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+ | 1.2333 | 1850 | 0.0001 | - |
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+ | 1.2667 | 1900 | 0.0001 | - |
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+ | 1.3 | 1950 | 0.0001 | - |
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+ | 1.3333 | 2000 | 0.0001 | - |
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+ | 1.3667 | 2050 | 0.0001 | - |
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+ | 1.4 | 2100 | 0.0001 | - |
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+ | 1.4333 | 2150 | 0.0001 | - |
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+ | 1.4667 | 2200 | 0.0001 | - |
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+ | 1.5 | 2250 | 0.0001 | - |
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+ | 1.5333 | 2300 | 0.0001 | - |
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+ | 1.5667 | 2350 | 0.0001 | - |
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+ | 1.6 | 2400 | 0.0 | - |
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+ | 1.6667 | 2500 | 0.0001 | - |
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+ | 1.7 | 2550 | 0.0 | - |
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+ | 1.7333 | 2600 | 0.0001 | - |
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+ | 1.8 | 2700 | 0.0001 | - |
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+ | 1.8333 | 2750 | 0.0001 | - |
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+ | 1.8667 | 2800 | 0.0001 | - |
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+ | 1.9 | 2850 | 0.0 | - |
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+ | 1.9333 | 2900 | 0.0001 | - |
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+ | 1.9667 | 2950 | 0.0 | - |
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+ | 2.0 | 3000 | 0.0 | - |
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+ | 2.0333 | 3050 | 0.0001 | - |
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+ | 2.0667 | 3100 | 0.0 | - |
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+ | 2.1 | 3150 | 0.0001 | - |
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+ | 2.1333 | 3200 | 0.0001 | - |
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+ | 2.1667 | 3250 | 0.0 | - |
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+ | 2.2 | 3300 | 0.0001 | - |
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+ | 2.2333 | 3350 | 0.0001 | - |
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+ | 2.2667 | 3400 | 0.0001 | - |
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+ | 2.3 | 3450 | 0.0 | - |
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+ | 2.3333 | 3500 | 0.0001 | - |
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+ | 2.3667 | 3550 | 0.0 | - |
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+ | 2.4 | 3600 | 0.0 | - |
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+ | 2.4333 | 3650 | 0.0 | - |
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+ | 2.4667 | 3700 | 0.0001 | - |
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+ | 2.5 | 3750 | 0.0 | - |
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+ | 2.5333 | 3800 | 0.0001 | - |
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+ | 2.5667 | 3850 | 0.0 | - |
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+ | 2.6 | 3900 | 0.0001 | - |
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+ | 2.6333 | 3950 | 0.0 | - |
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+ | 2.6667 | 4000 | 0.0001 | - |
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+ | 2.7 | 4050 | 0.0001 | - |
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+ | 2.7333 | 4100 | 0.0 | - |
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+ | 2.7667 | 4150 | 0.0 | - |
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+ | 2.8 | 4200 | 0.0 | - |
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+ | 2.8333 | 4250 | 0.0 | - |
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+ | 2.8667 | 4300 | 0.0 | - |
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+ | 2.9 | 4350 | 0.0 | - |
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+ | 2.9333 | 4400 | 0.0 | - |
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+ | 2.9667 | 4450 | 0.0001 | - |
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+ | 3.0 | 4500 | 0.0 | - |
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+ | 3.0333 | 4550 | 0.0001 | - |
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+ | 3.0667 | 4600 | 0.0 | - |
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+ | 3.1 | 4650 | 0.0 | - |
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+ | 3.1333 | 4700 | 0.0001 | - |
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+ | 3.1667 | 4750 | 0.0 | - |
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+ | 3.2 | 4800 | 0.0 | - |
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+ | 3.2333 | 4850 | 0.0 | - |
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+ | 3.2667 | 4900 | 0.0 | - |
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+ | 3.3 | 4950 | 0.0001 | - |
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+ | 3.3333 | 5000 | 0.0 | - |
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+ | 3.3667 | 5050 | 0.0 | - |
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+ | 3.4 | 5100 | 0.0001 | - |
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+ | 3.4333 | 5150 | 0.0 | - |
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+ | 3.4667 | 5200 | 0.0 | - |
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+ | 3.5 | 5250 | 0.0001 | - |
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+ | 3.5333 | 5300 | 0.0 | - |
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+ | 3.5667 | 5350 | 0.0 | - |
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+ | 3.6 | 5400 | 0.0 | - |
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+ | 3.6333 | 5450 | 0.0 | - |
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+ | 3.6667 | 5500 | 0.0 | - |
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+ | 3.7 | 5550 | 0.0001 | - |
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+ | 3.7333 | 5600 | 0.0 | - |
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+ | 3.7667 | 5650 | 0.0 | - |
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+ | 3.8 | 5700 | 0.0 | - |
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+ | 3.8333 | 5750 | 0.0 | - |
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+ | 3.8667 | 5800 | 0.0 | - |
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+ | 3.9 | 5850 | 0.0 | - |
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+ | 3.9333 | 5900 | 0.0 | - |
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+ | 3.9667 | 5950 | 0.0001 | - |
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+ | 4.0 | 6000 | 0.0 | - |
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  ### Framework Versions
287
  - Python: 3.9.16
config_setfit.json CHANGED
@@ -1,10 +1,11 @@
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  {
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- "normalize_embeddings": false,
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  "labels": [
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  "complaints",
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  "order tracking",
6
  "product discoverability",
 
7
  "product faq",
8
  "product policy"
9
- ]
 
10
  }
 
1
  {
 
2
  "labels": [
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  "complaints",
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  "order tracking",
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  "product discoverability",
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+ "product discoveribility",
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  "product faq",
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  "product policy"
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+ ],
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+ "normalize_embeddings": false
11
  }
model.safetensors CHANGED
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  size 437967672
 
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