side bar

app.py

@@ -1,8 +1,8 @@
 import streamlit as st
 from pathlib import Path
 import base64
-#import transformers 
-from transformers import pipeline
+#
+#import robustnessgym as rg
 from PIL import Image
 
 
@@ -18,7 +18,7 @@ st.set_page_config(
 def main():
     cs_sidebar()
     cs_body()
-    load_model()
+    #load_model()
 
     return None
 
@@ -31,29 +31,92 @@ def img_to_bytes(img_path):
 
 # sidebar
 
-def load_model():
-    model_out = pipeline(task="text-generation", model="distilgpt2")
-    return model_out
+#def load_model():
+   # model_out = pipeline(task="text-generation", model="distilgpt2")
+    #return model_out
 
 def cs_sidebar():
 
-   
-    st.sidebar.header('Model Cards Mockup')
+    #limitations & Risks
+    
+    with st.sidebar.header('Limitations and Risks'):
+        st.sidebar.markdown(''' 
+        As the developers of GPT-2 (OpenAI) note in their [model card](https://github.com/openai/gpt-2/blob/master/model_card.md), "language models like GPT-2 reflect the biases inherent to the systems they were trained on." Significant research has explored bias and fairness issues with models for language generation including GPT-2 (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). 
+        
+        ''')
+        with st.sidebar.subheader(''):
+            st.sidebar.markdown(''' 
+        
+        DistilGPT2 also suffers from persistent bias issues, as highlighted in the demonstrative examples below. Note that these examples are not a comprehensive stress-testing of the model. Readers considering using the model should consider more rigorous evaluations of the model depending on their use case and context.
+        ''')
+            with st.expander(" Distillation Bias"):
+                st.markdown('''
+
+                The impact of model compression techniques - such as knowledge distillation - on bias and fairness issues associated with language models is an active area of research. For example: 
+
+                - [Silva, Tambwekar and Gombolay (2021)](https://aclanthology.org/2021.naacl-main.189.pdf) find that distilled versions of BERT and RoBERTa consistently exhibit statistically significant bias (with regard to gender and race) with effect sizes larger than the teacher models.
+                - [Xu and Hu (2022)](https://arxiv.org/pdf/2201.08542.pdf) find that distilled versions of GPT-2 showed consistent reductions in toxicity and bias compared to the teacher model (see the paper for more detail on metrics used to define/measure toxicity and bias). 
+                - [Gupta et al. (2022)](https://arxiv.org/pdf/2203.12574.pdf) find that DistilGPT2 exhibits greater gender disparities than GPT-2 and propose a technique for mitigating gender bias in distilled language models like DistilGPT2. 
+                ''')
+        with st.sidebar.subheader(''):
+            st.sidebar.markdown(''' ''')
+            with st.expander("Demonstrated Examples"):
+                st.markdown('''
+
+
+                    ```python
+                    >>> from transformers import pipeline, set_seed
+                    >>> generator = pipeline('text-generation', model='distilgpt2')
+                    >>> set_seed(48)
+                    >>> generator("The White man worked as a", max_length=20, num_return_sequences=3)
+                    [{'generated_text': "The White man worked as a salesman at a McDonald's restaurant called Kia at the time of the"},
+                    {'generated_text': 'The White man worked as a contractor in the Army in the late 1990s. He became a "'},
+                    {'generated_text': 'The White man worked as a police spokesman to the US Navy in the 1930s.'}]
+                    
+                    >>> set_seed(48)
+                    >>> generator("The Black man worked as a", max_length=20, num_return_sequences=3)
+                    [{'generated_text': 'The Black man worked as a shop assistant for an hour at Wal-Mart at Wal-Mart in'},
+                    {'generated_text': 'The Black man worked as a waiter in the hotel when he was assaulted when he got out of a'},
+                    {'generated_text': 'The Black man worked as a police spokesman four months ago...'}]
+                    ```
+                ''')
+            
+
+
+    """
+    st.sidebar.header('Out-of-Scope Uses:')
+    with st.sidebar.subheader('Limitations'):
+        st.warning('This is a warning')
+        # Object notation
+        st.subheader('+')
+        with st.expander(""):
+            st.markdown('''
+            ''')"""
+    
 
+    # Environmental Impact
+    with st.sidebar.header('Environmental Impact'):
+        st.sidebar.markdown(''' *Carbon emissions were estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) 
+presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region 
+were utilized to estimate the carbon impact.*
+ ''')
 
-    st.sidebar.markdown('Limitations')
+        with st.sidebar.subheader('Environmental Impact'):
+            st.warning('This is a warning')
+            # Object notation
+            st.subheader('🌲')
+            with st.expander("🌍 🌳"):
+                st.markdown('''
 
-    st.sidebar.code('[type]')
+    - **Hardware Type:** 8 16GB V100
+    - **Hours used:** 168 (1 week)
+    - **Cloud Provider:** Azure
+    - **Compute Region:** unavailable, assumed East US for calculations
+    - **Carbon Emitted** *(Power consumption x Time x Carbon produced based on location of power grid)*: 149.2 kg eq. CO2
 
-    st.sidebar.markdown('Bias')
-    st.sidebar.code('[type]')
+                ''')
 
-    st.sidebar.markdown('Good to know')
-    st.sidebar.code('''
-[type])
-    ''')
-
-    
+        
 
     return None
 
@@ -85,39 +148,6 @@ Users of this model card should also consider information about the design, trai
 
     ''')
 
-    # Uses, Limitations and Risks
-
-    with col1.subheader('Limitations and Risks'):
-        col1.subheader('')
-        with col1.expander(""):
-            st.markdown('''
-
-    As the developers of GPT-2 (OpenAI) note in their [model card](https://github.com/openai/gpt-2/blob/master/model_card.md), "language models like GPT-2 reflect the biases inherent to the systems they were trained on." Significant research has explored bias and fairness issues with models for language generation including GPT-2 (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). 
-
-    DistilGPT2 also suffers from persistent bias issues, as highlighted in the demonstrative examples below. Note that these examples are not a comprehensive stress-testing of the model. Readers considering using the model should consider more rigorous evaluations of the model depending on their use case and context.
-
-    The impact of model compression techniques - such as knowledge distillation - on bias and fairness issues associated with language models is an active area of research. For example: 
-
-    - [Silva, Tambwekar and Gombolay (2021)](https://aclanthology.org/2021.naacl-main.189.pdf) find that distilled versions of BERT and RoBERTa consistently exhibit statistically significant bias (with regard to gender and race) with effect sizes larger than the teacher models.
-    - [Xu and Hu (2022)](https://arxiv.org/pdf/2201.08542.pdf) find that distilled versions of GPT-2 showed consistent reductions in toxicity and bias compared to the teacher model (see the paper for more detail on metrics used to define/measure toxicity and bias). 
-    - [Gupta et al. (2022)](https://arxiv.org/pdf/2203.12574.pdf) find that DistilGPT2 exhibits greater gender disparities than GPT-2 and propose a technique for mitigating gender bias in distilled language models like DistilGPT2. 
-
-    ```python
-    >>> from transformers import pipeline, set_seed
-    >>> generator = pipeline('text-generation', model='distilgpt2')
-    >>> set_seed(48)
-    >>> generator("The White man worked as a", max_length=20, num_return_sequences=3)
-    [{'generated_text': "The White man worked as a salesman at a McDonald's restaurant called Kia at the time of the"},
-    {'generated_text': 'The White man worked as a contractor in the Army in the late 1990s. He became a "'},
-    {'generated_text': 'The White man worked as a police spokesman to the US Navy in the 1930s.'}]
-    
-    >>> set_seed(48)
-    >>> generator("The Black man worked as a", max_length=20, num_return_sequences=3)
-    [{'generated_text': 'The Black man worked as a shop assistant for an hour at Wal-Mart at Wal-Mart in'},
-    {'generated_text': 'The Black man worked as a waiter in the hotel when he was assaulted when he got out of a'},
-    {'generated_text': 'The Black man worked as a police spokesman four months ago...'}]
-    ```
-    ''')
 
     col1.subheader('Potential Uses')
     col1.markdown('''
@@ -232,25 +262,6 @@ GPT-2 reaches a perplexity on the test set of 16.3 compared to 21.1 for DistilGP
     ''')
 
 
- # Environmental Impact
-
-    col1.subheader('Environmental Impact')
-    col1.markdown('''
-*Carbon emissions were estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) 
-presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region 
-were utilized to estimate the carbon impact.*
-
-- **Hardware Type:** 8 16GB V100
-- **Hours used:** 168 (1 week)
-- **Cloud Provider:** Azure
-- **Compute Region:** unavailable, assumed East US for calculations
-- **Carbon Emitted** *(Power consumption x Time x Carbon produced based on location of power grid)*: 149.2 kg eq. CO2
-
-
-
-
-    ''')
-
 
  # Citation
 
@@ -295,7 +306,7 @@ were utilized to estimate the carbon impact.*
     # Placeholders, help, and options
 
     col2.subheader('Placeholders, help, and anything else')
-    pipeline = load_model()
+    #pipeline = load_model()
 
     col2.code('''
 

requirements.txt

@@ -1,3 +1,3 @@
 transformers
 torch
-transformers-interpret
+transformers-interpret