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README.md
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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
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metrics:
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- accuracy
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widget:
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- text:
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When Stoic Farmer enters the battlefield, search your library for a basic
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card and reveal it. If an opponent controls more lands than you, put
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battlefield tapped. Otherwise put it into your hand. Then
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Foretell {1}{W} (During your turn, you may pay {2} and exile this card from
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hand face down. Cast it on a later turn for its foretell cost.)
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- text:
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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'
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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:
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type:
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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.
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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("
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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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It takes card name + text as a single str as input and outputs color identity as an encoding:
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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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# 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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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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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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## 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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