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@@ -23,6 +23,7 @@ checks. For more details, refer to our publicly available article.
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  > in the article for a more robust one, that also includes the commutative property of the meaning function. Namely, Meaning(Sent_a, Sent_b) = Meaning(Sent_b, Sent_a).
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  - [HuggingFace Model Card](https://huggingface.co/davebulaval/MeaningBERT)
 
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  ## Sanity Check
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@@ -68,8 +69,6 @@ model = AutoModelForSequenceClassification.from_pretrained("davebulaval/MeaningB
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  or you can use MeaningBERT as a metric for evaluation (no retrain) using the following with HuggingFace
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- ## Code Examples
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-
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  ```python
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  import torch
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@@ -92,6 +91,19 @@ with torch.no_grad():
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  print(scores.logits.tolist())
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  ```
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ------------------
 
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  > in the article for a more robust one, that also includes the commutative property of the meaning function. Namely, Meaning(Sent_a, Sent_b) = Meaning(Sent_b, Sent_a).
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  - [HuggingFace Model Card](https://huggingface.co/davebulaval/MeaningBERT)
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+ - [HuggingFace Metric Card](https://huggingface.co/spaces/davebulaval/meaningbert)
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  ## Sanity Check
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  or you can use MeaningBERT as a metric for evaluation (no retrain) using the following with HuggingFace
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  ```python
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  import torch
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  print(scores.logits.tolist())
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  ```
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+ or using our HuggingFace Metric module
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+
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+ ```python
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+ import evaluate
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+
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+ documents = ["He wanted to make them pay.", "This sandwich looks delicious.", "He wants to eat."]
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+ simplifications = ["He wanted to make them pay.", "This sandwich looks delicious.",
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+ "Whatever, whenever, this is a sentence."]
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+
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+ meaning_bert = evaluate.load("davebulaval/meaningbert")
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+
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+ print(meaning_bert.compute(documents=documents, simplifications=simplifications))
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+ ```
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