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End of training script.

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  1. README.md +63 -18
  2. model.safetensors +1 -1
README.md CHANGED
@@ -5,9 +5,35 @@ tags:
5
  - feature-extraction
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  - dense
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  - generated_from_trainer
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- - dataset_size:5
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  - loss:ContrastiveLoss
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  base_model: sentence-transformers/clip-ViT-B-32
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  pipeline_tag: sentence-similarity
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  library_name: sentence-transformers
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  ---
@@ -60,9 +86,9 @@ from sentence_transformers import SentenceTransformer
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  model = SentenceTransformer("philipp-zettl/MTGEmb-small")
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  # Run inference
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  sentences = [
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- 'The weather is lovely today.',
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- "It's so sunny outside!",
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- 'He drove to the stadium.',
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  ]
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  embeddings = model.encode(sentences)
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  print(embeddings.shape)
@@ -71,9 +97,9 @@ print(embeddings.shape)
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  # Get the similarity scores for the embeddings
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  similarities = model.similarity(embeddings, embeddings)
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  print(similarities)
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- # tensor([[1.0000, 0.9425, 0.8177],
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- # [0.9425, 1.0000, 0.8015],
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- # [0.8177, 0.8015, 1.0000]])
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  ```
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  <!--
@@ -118,19 +144,19 @@ You can finetune this model on your own dataset.
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  #### Unnamed Dataset
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- * Size: 5 training samples
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  * Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
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- * Approximate statistics based on the first 5 samples:
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- | | sentence_0 | sentence_1 | label |
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- |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------|
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- | type | string | string | int |
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- | details | <ul><li>min: 3 tokens</li><li>mean: 16.8 tokens</li><li>max: 66 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 53.6 tokens</li><li>max: 66 tokens</li></ul> | <ul><li>0: ~60.00%</li><li>1: ~40.00%</li></ul> |
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  * Samples:
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- | sentence_0 | sentence_1 | label |
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- |:-----------------------------|:------------------------------------------------------------------------------------------------------------|:---------------|
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- | <code>Veteran Armorer</code> | <code>https://cards.scryfall.io/normal/front/0/0/0000419b-0bba-4488-8f7a-6194544ce91e.jpg?1721427487</code> | <code>0</code> |
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- | <code>Forest</code> | <code>https://cards.scryfall.io/normal/front/0/0/0000419b-0bba-4488-8f7a-6194544ce91e.jpg?1721427487</code> | <code>1</code> |
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- | <code>Forest</code> | <code>Veteran Armorer</code> | <code>0</code> |
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  * Loss: [<code>ContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters:
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  ```json
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  {
@@ -270,6 +296,25 @@ You can finetune this model on your own dataset.
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  </details>
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  ### Framework Versions
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  - Python: 3.13.7
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  - Sentence Transformers: 5.1.2
 
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  - feature-extraction
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  - dense
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  - generated_from_trainer
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+ - dataset_size:149460
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  - loss:ContrastiveLoss
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  base_model: sentence-transformers/clip-ViT-B-32
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+ widget:
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+ - source_sentence: Meltdown
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+ sentences:
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+ - Ancient Imperiosaur
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+ - https://cards.scryfall.io/normal/front/1/9/192ccc7f-ffb1-4f78-8cf0-a220df612be7.jpg?1682536817
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+ - https://cards.scryfall.io/normal/front/5/6/56301392-3496-48d0-8d91-6b82e1164c98.jpg?1721427942
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+ - source_sentence: Etali, Primal Storm
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+ sentences:
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+ - https://cards.scryfall.io/normal/front/4/8/4874388e-0227-4b89-a986-d86c14482c81.jpg?1594065427
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+ - Battle of Wits
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+ - https://cards.scryfall.io/normal/front/1/d/1d3d8bb4-0430-45bb-930d-5d6db6521945.jpg?1587309687
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+ - source_sentence: Chrome Prowler
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+ sentences:
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+ - https://cards.scryfall.io/normal/front/a/2/a263f594-621e-46af-8561-f7eee565a19a.jpg?1562643297
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+ - https://cards.scryfall.io/normal/front/3/d/3dff363d-7e9f-4764-a9ee-ec2f23239df6.jpg?1562907900
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+ - https://cards.scryfall.io/normal/front/2/1/21121857-85b8-4ba8-9363-beafdb1005c2.jpg?1730486782
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+ - source_sentence: Beastbreaker of Bala Ged
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+ sentences:
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+ - https://cards.scryfall.io/normal/front/2/8/287ca034-9cea-4b84-98ba-76c24f038edb.jpg?1599709496
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+ - https://cards.scryfall.io/normal/front/5/4/547f2641-bcd6-4536-ba5a-f46170dd2803.jpg?1573513110
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+ - https://cards.scryfall.io/normal/front/4/c/4c29f6a1-42a5-433f-9c09-c160b096f8e1.jpg?1562542378
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+ - source_sentence: Against All Odds
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+ sentences:
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+ - https://cards.scryfall.io/normal/front/4/a/4ab2f81a-fcbe-44d1-8281-04dd78bb9ea3.jpg?1593274931
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+ - https://cards.scryfall.io/normal/front/3/c/3cd8dd4e-6892-49d7-8fae-97d04f9f6c84.jpg?1675956885
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+ - Sheltering Prayers
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  pipeline_tag: sentence-similarity
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  library_name: sentence-transformers
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  ---
 
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  model = SentenceTransformer("philipp-zettl/MTGEmb-small")
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  # Run inference
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  sentences = [
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+ 'Against All Odds',
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+ 'https://cards.scryfall.io/normal/front/3/c/3cd8dd4e-6892-49d7-8fae-97d04f9f6c84.jpg?1675956885',
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+ 'https://cards.scryfall.io/normal/front/4/a/4ab2f81a-fcbe-44d1-8281-04dd78bb9ea3.jpg?1593274931',
92
  ]
93
  embeddings = model.encode(sentences)
94
  print(embeddings.shape)
 
97
  # Get the similarity scores for the embeddings
98
  similarities = model.similarity(embeddings, embeddings)
99
  print(similarities)
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+ # tensor([[1.0000, 0.9248, 0.6695],
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+ # [0.9248, 1.0000, 0.6947],
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+ # [0.6695, 0.6947, 1.0000]])
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  ```
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  <!--
 
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  #### Unnamed Dataset
146
 
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+ * Size: 149,460 training samples
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  * Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
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+ * Approximate statistics based on the first 1000 samples:
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+ | | sentence_0 | sentence_1 | label |
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+ |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------|
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+ | type | string | string | int |
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+ | details | <ul><li>min: 3 tokens</li><li>mean: 17.16 tokens</li><li>max: 69 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 54.64 tokens</li><li>max: 69 tokens</li></ul> | <ul><li>0: ~57.60%</li><li>1: ~42.40%</li></ul> |
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  * Samples:
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+ | sentence_0 | sentence_1 | label |
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+ |:----------------------------------|:------------------------------------------------------------------------------------------------------------|:---------------|
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+ | <code>Comparative Analysis</code> | <code>https://cards.scryfall.io/normal/front/d/d/dd83129b-7e8c-4cc5-a7b3-e0ae221d7ad4.jpg?1562939549</code> | <code>1</code> |
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+ | <code>Breathkeeper Seraph</code> | <code>https://cards.scryfall.io/normal/front/1/b/1bdd3ecb-8c11-4a4c-a503-bc29f79a9dcb.jpg?1682204691</code> | <code>0</code> |
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+ | <code>Wei Infantry</code> | <code>https://cards.scryfall.io/normal/front/7/2/72c6465f-3144-4faf-b248-a9fb941dc002.jpg?1562257016</code> | <code>1</code> |
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  * Loss: [<code>ContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters:
161
  ```json
162
  {
 
296
 
297
  </details>
298
 
299
+ ### Training Logs
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+ | Epoch | Step | Training Loss |
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+ |:------:|:----:|:-------------:|
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+ | 0.2140 | 500 | 0.0342 |
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+ | 0.4281 | 1000 | 0.0311 |
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+ | 0.6421 | 1500 | 0.0306 |
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+ | 0.8562 | 2000 | 0.0302 |
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+ | 1.0702 | 2500 | 0.0287 |
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+ | 1.2842 | 3000 | 0.0262 |
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+ | 1.4983 | 3500 | 0.025 |
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+ | 1.7123 | 4000 | 0.0236 |
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+ | 1.9264 | 4500 | 0.022 |
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+ | 2.1404 | 5000 | 0.016 |
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+ | 2.3545 | 5500 | 0.0128 |
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+ | 2.5685 | 6000 | 0.0119 |
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+ | 2.7825 | 6500 | 0.0108 |
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+ | 2.9966 | 7000 | 0.0103 |
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+
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+
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  ### Framework Versions
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  - Python: 3.13.7
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  - Sentence Transformers: 5.1.2
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