mcmillanmajora commited on
Commit
ba3240e
2 Parent(s): 24d1e26 e258141

Merge pull request #6 from mcmillanmajora/add_streamlit

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Removed placeholder from model_exploration and streamlit version from…

Files changed (2) hide show
  1. posts/model_exploration.py +35 -5
  2. requirements.txt +1 -1
posts/model_exploration.py CHANGED
@@ -15,7 +15,23 @@ def run_article():
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  st.markdown("""
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  # Making a Hate Speech Detection Model
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- This is where design choices will go.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  # Model Output Ranking
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@@ -26,14 +42,23 @@ def run_article():
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  ["classla/roberta-base-frenk-hate",
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  "cardiffnlp/twitter-roberta-base-hate",
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  "Hate-speech-CNERG/dehatebert-mono-english"],
 
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  )
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  input_1 = st.text_input("Input 1",
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- placeholder="We shouldn't let [IDENTITY] suffer.")
 
 
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  input_2 = st.text_input("Input 2",
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- placeholder="I'd rather die than date [IDENTITY].")
 
 
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  input_3 = st.text_input("Input 3",
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- placeholder="Good morning.")
 
 
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  inputs = [input_1, input_2, input_3]
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  if st.form_submit_button(label="Rank inputs"):
@@ -61,7 +86,8 @@ def run_article():
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  ],
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  key='compare_model_2'
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  )
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- input_text = st.text_input("Comparison input")
 
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  if st.form_submit_button(label="Compare models"):
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  results = run_compare(model_name_1, model_name_2, input_text)
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  st.dataframe(results)
@@ -110,4 +136,8 @@ def run_compare(name_1, name_2, text):
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  return [out_1, out_2]
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  st.markdown("""
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  # Making a Hate Speech Detection Model
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+ Once the data has been collected using the definitions identified for the
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+ task, you can start training your model. At training, the model takes in
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+ the data with labels and learns the associated context in the input data
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+ for each label. Depending on the task design, the labels may be binary like
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+ 'hateful' and 'non-hateful' or multiclass like 'neutral', 'offensive', and
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+ 'attack'.
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+
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+ When presented with a new input string, the model then predicts the
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+ likelihood that the input is classified as each of the available labels and
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+ returns the label with the highest likelihood as well as how confident the
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+ model is in its selection using a score from 0 to 1.
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+
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+ Neural models such as transformers are frequently trained as general
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+ language models and then fine-tuned on specific classification tasks.
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+ These models can vary in their architecture and the optimization
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+ algorithms, sometimes resulting in very different output for the same
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+ input text.
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  # Model Output Ranking
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  ["classla/roberta-base-frenk-hate",
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  "cardiffnlp/twitter-roberta-base-hate",
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  "Hate-speech-CNERG/dehatebert-mono-english"],
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+ key="rank_model_select"
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  )
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+ # the placeholder key functionality was added in v1.2 of streamlit
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+ # and versions on Spaces currently goesup to v1.0
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  input_1 = st.text_input("Input 1",
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+ #placeholder="We shouldn't let [IDENTITY] suffer.",
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+ help= "Try a phrase like 'We shouldn't let [IDENTITY] suffer.'",
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+ key="rank_input_1")
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  input_2 = st.text_input("Input 2",
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+ #placeholder="I'd rather die than date [IDENTITY].",
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+ help= "Try a phrase like 'I'd rather die than date [IDENTITY].'",
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+ key="rank_input_2")
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  input_3 = st.text_input("Input 3",
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+ #placeholder="Good morning.",
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+ help= "Try a phrase like 'Good morning'",
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+ key="rank_input_3")
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  inputs = [input_1, input_2, input_3]
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  if st.form_submit_button(label="Rank inputs"):
 
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  ],
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  key='compare_model_2'
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  )
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+ input_text = st.text_input("Comparison input",
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+ key="compare_input")
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  if st.form_submit_button(label="Compare models"):
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  results = run_compare(model_name_1, model_name_2, input_text)
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  st.dataframe(results)
 
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  return [out_1, out_2]
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+ def main():
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+ run_article()
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+
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+ main()
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requirements.txt CHANGED
@@ -1,3 +1,3 @@
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- streamlit>=1.2.0
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  transformers
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  torch
 
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+ streamlit
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  transformers
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  torch