cifkao commited on
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f3cb237
1 Parent(s): bb204b7

Change default text

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  1. app.py +16 -2
app.py CHANGED
@@ -45,11 +45,25 @@ st.header("Context length probing")
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  model_name = st.selectbox("Model", ["distilgpt2", "gpt2", "EleutherAI/gpt-neo-125m"])
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  metric_name = st.selectbox("Metric", ["KL divergence", "Cross entropy"], index=1)
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- window_len = st.select_slider(r"Window size ($c_\text{max}$)", options=[8, 16, 32, 64, 128, 256, 512, 1024], value=512)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  text = st.text_area(
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  "Input text",
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- "The complex houses married and single soldiers and their families.",
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  )
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  if metric_name == "KL divergence":
 
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  model_name = st.selectbox("Model", ["distilgpt2", "gpt2", "EleutherAI/gpt-neo-125m"])
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  metric_name = st.selectbox("Metric", ["KL divergence", "Cross entropy"], index=1)
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+ window_len = st.select_slider(
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+ r"Window size ($c_\text{max}$)",
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+ options=[8, 16, 32, 64, 128, 256, 512, 1024],
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+ value=512
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+ )
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+
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+ DEFAULT_TEXT = """
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+ We present context length probing, a novel explanation technique for causal
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+ language models, based on tracking the predictions of a model as a function of the length of
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+ available context, and allowing to assign differential importance scores to different contexts.
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+ The technique is model-agnostic and does not rely on access to model internals beyond computing
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+ token-level probabilities. We apply context length probing to large pre-trained language models
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+ and offer some initial analyses and insights, including the potential for studying long-range
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+ dependencies.
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+ """.replace("\n", " ").strip()
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  text = st.text_area(
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  "Input text",
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+ DEFAULT_TEXT,
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  )
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  if metric_name == "KL divergence":