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Add SetFit model

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  1. README.md +16 -23
  2. model.safetensors +1 -1
  3. model_head.pkl +1 -1
README.md CHANGED
@@ -11,21 +11,14 @@ datasets:
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  metrics:
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  - accuracy
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  widget:
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- - text: WOW THIS GUY TALKS LIKE A RETARD HAHAHAHAHAH
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- - text: American people are dumbass. They don't even know geography
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- - text: Oh GOD YES!!!! Feel the shrapnel pierce through your skin, let the fire consume
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- your flesh and lay lifeless on the unforgiving sand that seeps into your wounds.
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- Your screams is what gives Murcia the fuck yeah in the most patriotic songs on
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- earth. BURN IN HELL YOU GOATS !!!
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- - text: One needs to do a Pareto Analysis...90-95% of global terrorists belong to
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- a single religion that represents 25% of the global population (1.25 billion in
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- a 8 billion global population). To say that terrorism has no religion and colour,
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- is the biggest bluff in the world. If Islam was such a peaceful religion, then
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- why are refugees from Islamic countries seek non-Muslim countries for settlement....it
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- is because they want to Islamise the world.
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- - text: It's so sick and sad that young people will think of the concept of clout
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- like rap niggers invented it because they aren't even properly taught about the
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- english language anymore.
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  pipeline_tag: text-classification
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  inference: false
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  model-index:
@@ -40,7 +33,7 @@ model-index:
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  split: test
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  metrics:
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  - type: accuracy
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- value: 0.39466666666666667
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  name: Accuracy
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  ---
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@@ -76,7 +69,7 @@ The model has been trained using an efficient few-shot learning technique that i
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  ### Metrics
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  | Label | Accuracy |
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  |:--------|:---------|
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- | **all** | 0.3947 |
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  ## Uses
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@@ -96,7 +89,7 @@ from setfit import SetFitModel
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  # Download from the 🤗 Hub
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  model = SetFitModel.from_pretrained("amitprgx/setfit-ethos-multilabel-example")
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  # Run inference
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- preds = model("WOW THIS GUY TALKS LIKE A RETARD HAHAHAHAHAH")
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  ```
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  <!--
@@ -128,7 +121,7 @@ preds = model("WOW THIS GUY TALKS LIKE A RETARD HAHAHAHAHAH")
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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 | 2 | 16.3594 | 72 |
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  ### Training Hyperparameters
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  - batch_size: (16, 16)
@@ -151,10 +144,10 @@ preds = model("WOW THIS GUY TALKS LIKE A RETARD HAHAHAHAHAH")
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  ### Training Results
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  | Epoch | Step | Training Loss | Validation Loss |
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  |:------:|:----:|:-------------:|:---------------:|
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- | 0.0063 | 1 | 0.2956 | - |
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- | 0.3125 | 50 | 0.1327 | - |
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- | 0.625 | 100 | 0.0963 | - |
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- | 0.9375 | 150 | 0.0525 | - |
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  ### Framework Versions
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  - Python: 3.10.12
 
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  metrics:
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  - accuracy
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  widget:
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+ - text: Roses are red. Your sweatshirt is blue. When a white girl gets raped, the
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+ suspect for sure is you!
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+ - text: The Jew Faggot Behind The Financial Collapse.
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+ - text: Magnificient. Contrary to what the media reports, I have hope for the people
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+ of Europe to expel these leeches.
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+ - text: I'd get my money back if I were you. Your sex change was a complete failure.
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+ - text: If only there was a way to get children without having to get women pregnant
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+ we had them all ready killed
 
 
 
 
 
 
 
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  pipeline_tag: text-classification
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  inference: false
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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: 0.2857142857142857
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  name: Accuracy
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  ---
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  ### Metrics
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  | Label | Accuracy |
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  |:--------|:---------|
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+ | **all** | 0.2857 |
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  ## Uses
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  # Download from the 🤗 Hub
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  model = SetFitModel.from_pretrained("amitprgx/setfit-ethos-multilabel-example")
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  # Run inference
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+ preds = model("The Jew Faggot Behind The Financial Collapse.")
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  ```
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  <!--
 
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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 | 3 | 17.4062 | 87 |
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  ### Training Hyperparameters
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  - batch_size: (16, 16)
 
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  ### Training Results
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  | Epoch | Step | Training Loss | Validation Loss |
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  |:------:|:----:|:-------------:|:---------------:|
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+ | 0.0063 | 1 | 0.3199 | - |
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+ | 0.3125 | 50 | 0.0952 | - |
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+ | 0.625 | 100 | 0.1276 | - |
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+ | 0.9375 | 150 | 0.0956 | - |
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  ### Framework Versions
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  - Python: 3.10.12
model.safetensors CHANGED
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