adding data and initial plots

app.py

@@ -0,0 +1,59 @@
+import gradio as gr
+import pandas as pd
+import os
+import plotly.express as px
+import numpy as np
+
+datadir = 'data/emissions/complete'
+
+model_param_df = pd.read_csv('data/model_parameters.csv', header=0)
+model_performance_df = pd.read_csv('data/performance.csv', header=0)
+emissions_df = pd.read_csv('data/co2_data.csv',header=0)
+modalities_df = pd.read_csv('data/modalities_data.csv',header=0)
+finetuned_df = emissions_df[~emissions_df['task'].str.contains('zero')]
+
+fig0 = px.scatter(finetuned_df, x="dataset", y="query emissions (g)", color="model", log_y=True)
+fig0.update_layout(xaxis={'categoryorder':'mean ascending'})
+fig0.update_layout(yaxis_title='Total carbon emitted (g)')
+fig0.update_layout(xaxis_title='Dataset')
+
+
+fig1 = px.box(finetuned_df, x="task", y="query_energy (kWh)", color="task", log_y=True)
+fig1.update_layout(xaxis={'categoryorder':'mean ascending'})
+fig1.update_layout(yaxis_title='Total energy used (Wh)')
+fig1.update_layout(xaxis_title='Task')
+
+fig2 = px.scatter(modalities_df, x="num_params", y="query emissions (g)", color="modality",
+             log_x=True, log_y=True, custom_data=['model','task'])
+
+fig2.update_traces(
+    hovertemplate="<br>".join([
+        "Model: %{customdata[0]}",
+        "Task: %{customdata[1]}",
+    ])
+)
+fig2.update_layout(xaxis_title='Model size (number of parameters)')
+fig2.update_layout(yaxis_title='Model emissions (g of CO<sub>2</sub>)')
+
+
+demo = gr.Blocks()
+
+with demo:
+    gr.Markdown("# CO2 Inference Demo")
+    gr.Markdown("## Explore the plots below to get more insights about the different models and tasks from our study.")
+    with gr.Row():
+        with gr.Column():
+            gr.Plot(fig0)
+    with gr.Row():
+        with gr.Column():
+            gr.Plot(fig1)
+    with gr.Row():
+        with gr.Column():
+            gr.Plot(fig2)
+
+
+
+
+
+
+demo.launch()

data/model_parameters.csv

@@ -0,0 +1,117 @@
+model_name,num_parameters
+ainize/kobart-news,123859968
+albert-base-v2,11683584
+alvaroalon2/biobert_diseases_ner,108310272
+Babelscape/wikineural-multilingual-ner,177853440
+bert-base-cased,108310272
+bert-base-uncased,109482240
+bert-large-uncased-whole-word-masking-finetuned-squad,335141888
+bigscience/bloomz-560m,559214592
+bigscience/bloom-560m,559214592
+cardiffnlp/twitter-roberta-base-irony,124645632
+cardiffnlp/twitter-roberta-base-sentiment-latest,124645632
+deepset/bert-large-uncased-whole-word-masking-squad2,335141888
+deepset/electra-base-squad2,108891648
+deepset/roberta-base-squad2,124645632
+deepset/tinyroberta-squad2,82118400
+distilbert-base-cased-distilled-squad,65190912
+distilbert-base-multilingual-cased,134734080
+distilbert-base-uncased,66362880
+distilbert-base-uncased-distilled-squad,66362880
+distilgpt2,81912576
+dslim/bert-base-NER,108310272
+dslim/bert-large-NER,333579264
+EleutherAI/gpt-neo-125m,125198592
+facebook/bart-large-cnn,406290432
+facebook/convnextv2-tiny-1k-224,27866496
+facebook/convnextv2-tiny-22k-384,27866496
+facebook/detr-resnet-101,60308416
+facebook/detr-resnet-50,41368512
+facebook/opt-1.3b,1315758080
+facebook/opt-6.7b,6658473984
+google/mobilenet_v1_0.75_192,1816560
+google/pegasus-xsum,569748480
+google/vit-base-patch16-224,86389248
+google/vit-base-patch16-384,86681088
+gpt2,124439808
+gpt2-medium,354823168
+gpt2-xl,1557611200
+hustvl/yolos-small,30204288
+hustvl/yolos-tiny,6359040
+j-hartmann/emotion-english-distilroberta-base,82118400
+Jean-Baptiste/roberta-large-ner-english,355359744
+joeddav/xlm-roberta-large-xnli,559890432
+jozhang97/deta-swin-large,217683380
+martin-ha/toxic-comment-model,66362880
+microsoft/beit-base-patch16-224-pt22k-ft22k,85761984
+microsoft/deberta-base,138601728
+microsoft/git-base,153147648
+microsoft/git-large-coco,370724608
+microsoft/resnet-18,11176512
+microsoft/resnet-50,23508032
+MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli,183831552
+nlpconnect/vit-gpt2-image-captioning,239195904
+nlptown/bert-base-multilingual-uncased-sentiment,167356416
+obi/deid_roberta_i2b2,355359744
+oliverguhr/fullstop-punctuation-multilang-large,559890432
+philschmid/bart-large-cnn-samsum,406290432
+polejowska/detr-r50-cd45rb-1ah-6l,41368512
+polejowska/detr-r50-cd45rb-8ah-6l-gelu-corrected,41368512
+QCRI/bert-base-multilingual-cased-pos-english,177853440
+Rakib/roberta-base-on-cuad,124645632
+roberta-base,124645632
+Salesforce/blip-image-captioning-base,224726017
+Salesforce/blip-image-captioning-large,447175937
+Salesforce/blip2-flan-t5-xl,3942446592
+Salesforce/blip2-opt-2.7b,3744679936
+SamLowe/roberta-base-go_emotions,124645632
+SenseTime/deformable-detr,39913666
+sshleifer/distilbart-cnn-12-6,305510400
+t5-base,222903552
+t5-large,737668096
+t5-small,60506624
+xlm-roberta-base,278043648
+bhadresh-savani/distilbert-base-uncased-emotion,66362880
+bert-base-multilingual-cased,177853440
+ydshieh/vit-gpt2-coco-en,124439808
+ml6team/keyphrase-extraction-distilbert-inspec,66365187
+runwayml/stable-diffusion-v1-5,1370216895
+stabilityai/stable-diffusion-2-1,1289952427
+stabilityai/stable-diffusion-xl-base-1.0,3468837867
+SG161222/Realistic_Vision_V1.4,1370216895
+CompVis/stable-diffusion-v1-4,1370216895
+stabilityai/stable-diffusion-2-1-base,1289952427
+prompthero/openjourney,1370216895
+dreamlike-art/dreamlike-photoreal-2.0,1066235307
+warp-ai/wuerstchen,1426756238
+gpt2,124439808
+facebook/opt-1.3b,1315758080
+meta-llama/Llama-2-7b-hf,6607343616
+google/flan-t5-xxl,11003736064
+google/flan-t5-xl,2783959040
+google/flan-t5-large,750251008
+google/flan-t5-base,222903552
+sshleifer/distilbart-xsum-12-6,229933056
+sshleifer/distilbart-cnn-12-6,305510400
+pszemraj/led-large-book-summary,459801600
+google/pegasus-xsum,569748480
+google/pegasus-large,570797056
+google/bigbird-pegasus-large-arxiv,576891904
+facebook/bart-large-cnn,406290432
+ainize/bart-base-cnn,139420416
+lvwerra/distilbert-imdb,66362880
+sbcBI/sentiment_analysis_model,66362880
+distilbert-base-uncased-finetuned-sst-2-english,66362880
+cardiffnlp/twitter-xlm-roberta-base-sentiment,278043648
+finiteautomata/bertweet-base-sentiment-analysis,134899968
+siebert/sentiment-roberta-large-english,355359744
+timpal0l/mdeberta-v3-base-squad2,278218752
+google/pegasus-multi_news,570797056
+elastic/distilbert-base-cased-finetuned-conll03-english,65190912
+dmargutierrez/distilbert-base-multilingual-cased-mapa_coarse-ner,134734080
+xlm-roberta-large-finetuned-conll03-english,559890432
+nota-ai/bk-sdm-tiny,834080895
+segmind/tiny-sd,530099307
+bigscience/bloomz-1b7,1720000000
+bigscience/bloomz-7b1,7000000000
+bigscience/bloomz-3b,3000000000

data/performance.csv

@@ -0,0 +1,49 @@
+model,task,type,sst2 (acc),imdb (acc),tomatoes (acc),sciq (acc),squad (f1),"squad_v2 (f1, has answer)",samsum (rouge),xsum (rouge),cnn (rouge)
+bigscience/bloomz-560m,sentiment,decoder,0.9243,0.9409,0.848,,,,,,
+bigscience/bloomz-560m,summarization,decoder,,,,,,,0.2253,0.1463,0.1017
+bigscience/bloomz-560m,qa,decoder,,,,0.9180,0.4269,0.209,,,
+bigscience/bloomz-1b7,sentiment,decoder,0.9438,0.9724,0.9296,,,,,,
+bigscience/bloomz-1b7,summarization,decoder,,,,,,,0.257,0.1553,0.1803
+bigscience/bloomz-1b7,qa,decoder,,,,0.9590,0.5033,0.248,,,
+bigscience/bloomz-3b,sentiment,decoder,0.9472,0.9778,0.9493,,,,,,
+bigscience/bloomz-3b,summarization,decoder,,,,,,,0.2792,0.1722,0.211
+bigscience/bloomz-3b,qa,decoder,,,,0.9670,0.5341,0.2639,,,
+bigscience/bloomz-7b1,sentiment,decoder,0.9438,0.9786,0.9465,,,,,,
+bigscience/bloomz-7b1,summarization,decoder,,,,,,,0.3183,0.2145,0.0941
+bigscience/bloomz-7b1,qa,decoder,,,,0.966,0.5388,0.2667,,,
+google/flan-t5-xxl,sentiment,seq2seq,0.9553,0.9691,0.9193,,,,,,
+google/flan-t5-xxl,qa,seq2seq,,,,0.7210,0.9754,0.4886,,,
+google/flan-t5-xxl,summarization,seq2seq,,,,,,,0.30332,0.37269,0.2281
+google/flan-t5-xl,sentiment,seq2seq,0.9564,0.9663,0.9259,,,,,,
+google/flan-t5-xl,qa,seq2seq,,,,0.6550,0.968,0.4867,,,
+google/flan-t5-xl,summarization,seq2seq,,,,,,,0.4919,0.3844,0.2371
+google/flan-t5-large,sentiment,seq2seq,0.9438,0.9634,0.9174,,,,,,
+google/flan-t5-large,qa,seq2seq,,,,0.5340,0.9683,0.5019,,,
+google/flan-t5-large,summarization,seq2seq,,,,,,,0.4539,0.3047,0.2408
+google/flan-t5-base,sentiment,seq2seq,0.9289,0.9466,0.8846,,,,,,
+google/flan-t5-base,qa,seq2seq,,,,0.6100,0.9487,0.477,,,
+google/flan-t5-base,summarization,seq2seq,,,,,,,0.4569,0.322,0.2336
+distilbert-base-uncased-distilled-squad,qa,encoder-finetuned,,,,0.4434,0.8655,0.8621,,,
+distilbert-base-cased-distilled-squad,qa,encoder-finetuned,,,,0.4615,0.8699,0.8694,,,
+deepset/roberta-base-squad2,qa,encoder-finetuned,,,,0.4785,0.9315,0.8295,,,
+bert-large-uncased-whole-word-masking-finetuned-squad,qa,encoder-finetuned,,,,0.4774,0.9300,0.8388,,,
+timpal0l/mdeberta-v3-base-squad2,qa,encoder-finetuned,,,,0.4604,0.9059,0.9037,,,
+deepset/tinyroberta-squad2,qa,encoder-finetuned,,,,0.4536,0.9793,0.9056,,,
+deepset/electra-base-squad2,qa,encoder-finetuned,,,,0.4830,0.8882,0.8171,,,
+deepset/bert-large-uncased-whole-word-masking-squad2,qa,encoder-finetuned,,,,0.4638,0.9250,0.9240,,,
+sshleifer/distilbart-xsum-12-6,summarization,seq2seq-finetuned,,,,,,,0.203249,0.452877,0.230331
+sshleifer/distilbart-cnn-12-6,summarization,seq2seq-finetuned,,,,,,,0.291424,0.210314,0.44241
+pszemraj/led-large-book-summary,summarization,seq2seq-finetuned,,,,,,,0.334514,0.162446,0.328774
+google/pegasus-xsum,summarization,seq2seq-finetuned,,,,,,,0.219676,0.218096,0.222062
+google/pegasus-large,summarization,seq2seq-finetuned,,,,,,,0.270341,0.174476,0.342469
+google/pegasus-multi_news,summarization,seq2seq-finetuned,,,,,,,0.11537,0.16396,0.290169
+facebook/bart-large-cnn,summarization,seq2seq-finetuned,,,,,,,0.315257,0.207919,0.440558
+ainize/bart-base-cnn,summarization,seq2seq-finetuned,,,,,,,0.270195,0.15911,0.259687
+distilbert-base-uncased-finetuned-sst-2-english,sentiment,encoder-finetuned,0.9885521686,0.88412,0.8968105066,,,,,,
+nlptown/bert-base-multilingual-uncased-sentiment,sentiment,encoder-finetuned,0.752293578,0.84824,0.7307692308,,,,,,
+twitter-roberta-base-sentiment-latest,sentiment,encoder-finetuned,0.8199541284,0.7952,0.7664165103,,,,,,
+cardiffnlp/twitter-xlm-roberta-base-sentiment,sentiment,encoder-finetuned,0.7912844037,0.70856,0.7420262664,,,,,,
+lvwerra/distilbert-imdb,sentiment,encoder-finetuned,0.878440367,0.928,0.8151969981,,,,,,
+siebert/sentiment-roberta-large-english,sentiment,encoder-finetuned,0.9243119266,0.91616,0.9202626642,,,,,,
+finiteautomata/bertweet-base-sentiment-analysis,sentiment,encoder-finetuned,0.8188073394,0.72068,0.7739212008,,,,,,
+sbcBI/sentiment_analysis_model,sentiment,encoder-finetuned,0.8061926606,0.74636,0.7636022514,,,,,,