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  ---
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  library_name: setfit
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  tags:
 
 
 
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  - setfit
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  - sentence-transformers
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  - text-classification
@@ -8,34 +11,37 @@ tags:
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  metrics:
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  - accuracy
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  widget:
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- - text: 'Stoic Farmer
 
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- When Stoic Farmer enters the battlefield, search your library for a basic Plains
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- card and reveal it. If an opponent controls more lands than you, put it onto the
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- battlefield tapped. Otherwise put it into your hand. Then shuffle.
 
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- Foretell {1}{W} (During your turn, you may pay {2} and exile this card from your
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- hand face down. Cast it on a later turn for its foretell cost.)'
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- - text: 'Hibernation Sliver
 
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- All Slivers have "Pay 2 life: Return this permanent to its owner''s hand."'
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- - text: 'Scattershot Archer
 
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-
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- {T}: Scattershot Archer deals 1 damage to each creature with flying.'
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- - text: 'Seize the Initiative
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-
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-
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- Target creature gets +1/+1 and gains first strike until end of turn.'
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- - text: 'Voldaren Duelist
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  Haste
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- When Voldaren Duelist enters the battlefield, target creature can''t block this
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- turn.'
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  pipeline_tag: text-classification
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  inference: false
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  base_model: sentence-transformers/paraphrase-mpnet-base-v2
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  type: text-classification
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  name: Text Classification
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  dataset:
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- name: Unknown
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- type: unknown
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  split: test
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  metrics:
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  - type: accuracy
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  value: 0.7145687016027372
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  name: Accuracy
 
 
 
 
 
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  ---
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  # SetFit with sentence-transformers/paraphrase-mpnet-base-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 [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) as the Sentence Transformer embedding model. A OneVsRestClassifier instance is used for classification.
@@ -89,6 +112,48 @@ The model has been trained using an efficient few-shot learning technique that i
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  |:--------|:---------|
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  | **all** | 0.7146 |
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  ## Uses
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  ### Direct Use for Inference
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  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("Scattershot Archer
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  ---
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  library_name: setfit
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  tags:
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+ - mtg
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+ - multilabel
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+ - magic
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  - setfit
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  - sentence-transformers
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  - text-classification
 
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  metrics:
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  - accuracy
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  widget:
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+ - text: >-
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+ Stoic Farmer
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+ When Stoic Farmer enters the battlefield, search your library for a basic
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+ Plains card and reveal it. If an opponent controls more lands than you, put
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+ it onto the battlefield tapped. Otherwise put it into your hand. Then
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+ shuffle.
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+ Foretell {1}{W} (During your turn, you may pay {2} and exile this card from
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+ your hand face down. Cast it on a later turn for its foretell cost.)
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+ - text: |-
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+ Hibernation Sliver
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+ All Slivers have "Pay 2 life: Return this permanent to its owner's hand."
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+ - text: |-
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+ Scattershot Archer
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+ {T}: Scattershot Archer deals 1 damage to each creature with flying.
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+ - text: |-
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+ Seize the Initiative
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+ Target creature gets +1/+1 and gains first strike until end of turn.
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+ - text: >-
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+ Voldaren Duelist
 
 
 
 
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  Haste
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+ When Voldaren Duelist enters the battlefield, target creature can't block
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+ this turn.
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  pipeline_tag: text-classification
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  inference: false
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  base_model: sentence-transformers/paraphrase-mpnet-base-v2
 
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  type: text-classification
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  name: Text Classification
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  dataset:
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+ name: mtg-coloridentity-multilabel-classification
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+ type: joshuasundance/mtg-coloridentity-multilabel-classification
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  split: test
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  metrics:
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  - type: accuracy
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  value: 0.7145687016027372
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  name: Accuracy
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+ license: mit
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+ datasets:
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+ - joshuasundance/mtg-coloridentity-multilabel-classification
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+ language:
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+ - en
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  ---
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+ This is a proof-of-concept model trained on `datasets/joshuasundance/mtg-coloridentity-multilabel-classification`.
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+
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+ It takes card name + text as a single str as input and outputs color identity as an encoding:
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+
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+ ```python
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+ colors = ['B', 'G', 'R', 'U', 'W']
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+ b = [1, 0, 0, 0, 0]
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+ bw = [1, 0, 0, 0, 1]
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+ gru = [0, 1, 1, 1, 0]
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+ # and so on
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+ ```
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+
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  # SetFit with sentence-transformers/paraphrase-mpnet-base-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 [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) as the Sentence Transformer embedding model. A OneVsRestClassifier instance is used for classification.
 
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  |:--------|:---------|
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  | **all** | 0.7146 |
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+ ### Classification Report
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+ ```text
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+ precision recall f1-score support
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+
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+ 0.80 0.77 0.78 594
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+ B 0.81 0.76 0.78 821
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+ BG 0.42 0.56 0.48 63
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+ BGR 0.46 0.55 0.50 22
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+ BGRU 0.00 0.00 0.00 0
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+ BGRUW 0.73 0.33 0.46 24
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+ BGRW 0.00 0.00 0.00 0
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+ BGU 0.27 0.38 0.32 8
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+ BGUW 0.12 1.00 0.22 1
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+ BGW 0.14 0.33 0.19 9
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+ BR 0.41 0.59 0.48 80
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+ BRU 0.55 0.50 0.52 24
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+ BRUW 0.00 0.00 0.00 0
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+ BRW 0.29 0.36 0.32 14
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+ BU 0.53 0.56 0.54 91
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+ BUW 0.29 0.43 0.34 14
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+ BW 0.36 0.37 0.37 73
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+ G 0.77 0.76 0.77 791
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+ GR 0.42 0.46 0.44 85
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+ GRU 0.14 0.22 0.17 9
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+ GRUW 0.00 0.00 0.00 0
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+ GRW 0.27 0.50 0.35 18
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+ GU 0.48 0.49 0.49 69
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+ GUW 0.15 0.27 0.20 15
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+ GW 0.40 0.43 0.41 89
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+ R 0.81 0.77 0.79 803
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+ RU 0.43 0.51 0.47 68
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+ RUW 0.20 0.43 0.27 7
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+ RW 0.47 0.49 0.48 80
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+ U 0.83 0.81 0.82 818
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+ UW 0.37 0.43 0.40 86
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+ W 0.77 0.72 0.74 777
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+
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+ accuracy 0.71 5553
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+ macro avg 0.40 0.46 0.41 5553
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+ weighted avg 0.73 0.71 0.72 5553
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+ ```
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+
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  ## Uses
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  ### Direct Use for Inference
 
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  from setfit import SetFitModel
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  # Download from the 🤗 Hub
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+ model = SetFitModel.from_pretrained("joshuasundance/mtg-coloridentity-multilabel-classification")
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  # Run inference
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  preds = model("Scattershot Archer
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