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Update README.md

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@@ -10,6 +10,7 @@ pipeline_tag: text-classification
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  base_model: "distilbert/distilbert-base-uncased"
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  widget:
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  - text: "Left context. [SEP] Right context."
 
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  ---
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  # DistilBERT Cross Segment Document Chunking
@@ -43,16 +44,16 @@ model = AutoModelForSequenceClassification.from_pretrained(
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  pairs = [
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  "Left context. [SEP] Right context.",
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- "he also earned five mvp stars with the martian men's tenis team in 2149. [SEP] mart jhones spent the 2166 and 2167 seasons with the all stars intergalactic in the interstelar soccer league ( isl ).",
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  ]
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  pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer, return_all_scores=True)
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  pipe(pairs)
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- [[{'label': 'SAME', 'score': 0.9845659136772156},
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- {'label': 'DIFFERENT', 'score': 0.015434039756655693}],
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- [{'label': 'SAME', 'score': 0.44031277298927307},
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- {'label': 'DIFFERENT', 'score': 0.5596872568130493}]]
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  ```
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  ## Training Data
 
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  base_model: "distilbert/distilbert-base-uncased"
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  widget:
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  - text: "Left context. [SEP] Right context."
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+ - text: "They have 6 grandchildren. [SEP] Ane is currently coaching Crestwood High School's Boys Varsity Soccer."
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  ---
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  # DistilBERT Cross Segment Document Chunking
 
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  pairs = [
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  "Left context. [SEP] Right context.",
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+ "They have 6 grandchildren. [SEP] Ane is currently coaching Crestwood High School's Boys Varsity Soccer.",
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  ]
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  pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer, return_all_scores=True)
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  pipe(pairs)
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+ [[{'label': 'SAME', 'score': 0.986},
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+ {'label': 'DIFFERENT', 'score': 0.015}],
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+ [{'label': 'SAME', 'score': 0.212},
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+ {'label': 'DIFFERENT', 'score': 0.788}]]
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  ```
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  ## Training Data