update

.gitattributes

@@ -1,32 +1,2 @@
-*.7z filter=lfs diff=lfs merge=lfs -text
-*.arrow filter=lfs diff=lfs merge=lfs -text
-*.bin filter=lfs diff=lfs merge=lfs -text
-*.bz2 filter=lfs diff=lfs merge=lfs -text
-*.ftz filter=lfs diff=lfs merge=lfs -text
-*.gz filter=lfs diff=lfs merge=lfs -text
-*.h5 filter=lfs diff=lfs merge=lfs -text
-*.joblib filter=lfs diff=lfs merge=lfs -text
-*.lfs.* filter=lfs diff=lfs merge=lfs -text
-*.model filter=lfs diff=lfs merge=lfs -text
-*.msgpack filter=lfs diff=lfs merge=lfs -text
-*.onnx filter=lfs diff=lfs merge=lfs -text
-*.ot filter=lfs diff=lfs merge=lfs -text
-*.parquet filter=lfs diff=lfs merge=lfs -text
-*.pb filter=lfs diff=lfs merge=lfs -text
-*.pt filter=lfs diff=lfs merge=lfs -text
-*.pth filter=lfs diff=lfs merge=lfs -text
-*.rar filter=lfs diff=lfs merge=lfs -text
-saved_model/**/* filter=lfs diff=lfs merge=lfs -text
-*.tar.* filter=lfs diff=lfs merge=lfs -text
-*.tflite filter=lfs diff=lfs merge=lfs -text
-*.tgz filter=lfs diff=lfs merge=lfs -text
-*.wasm filter=lfs diff=lfs merge=lfs -text
-*.xz filter=lfs diff=lfs merge=lfs -text
-*.zip filter=lfs diff=lfs merge=lfs -text
-*.zstandard filter=lfs diff=lfs merge=lfs -text
-*tfevents* filter=lfs diff=lfs merge=lfs -text
-models/bertopic_model_tokyo_olympics_tweets filter=lfs diff=lfs merge=lfs -text
-models/bertopic_model_tokyo_olympics_tweets_unclean filter=lfs diff=lfs merge=lfs -text
-models/distilbart-mnli-12-1/flax_model.msgpack filter=lfs diff=lfs merge=lfs -text
 models/distilbart-mnli-12-1/pytorch_model.bin filter=lfs diff=lfs merge=lfs -text
-models/distilbert-base-uncased-finetuned-sst-2-english/pytorch_model.bin filter=lfs diff=lfs merge=lfs -text
+models/distilbert-base-uncased-finetuned-sst-2-english/pytorch_model.bin filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -1,7 +1,5 @@
 # custom
 survey_analytics.ipynb
-embeddings_unclean.pickle
-embeddings.pickle
 
 # Byte-compiled / optimized / DLL files
 __pycache__/

README.md

@@ -1,13 +1,2 @@
----
-title: Survey Analytics
-emoji: 🐨
-colorFrom: green
-colorTo: indigo
-sdk: streamlit
-sdk_version: 1.10.0
-app_file: app.py
-pinned: false
-license: gpl-3.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# survey_analytics
+Survey Analytic Techniques

app.py

@@ -9,6 +9,7 @@ import os
 import matplotlib.pyplot as plt
 import seaborn as sns
 import plotly.express as px
+import pickle
 
 # factor analysis
 from factor_analyzer import FactorAnalyzer
@@ -19,7 +20,6 @@ from scipy.stats import zscore
 # nlp
 from bertopic import BERTopic
 from transformers import pipeline
-import transformers
 
 # custom
 import survey_analytics_library as LIB
@@ -40,16 +40,10 @@ def read_survey_data():
 data_survey, data_questions = read_survey_data()
 
 @st.cache
-def read_tweet_data():
+def read_tokyo_data():
     tokyo = pd.read_csv(data_path+'tokyo_olympics_tweets.csv')
     return tokyo
-tokyo = read_tweet_data()
-
-@st.cache(allow_output_mutation=True)
-def load_bertopic_model_unclean():
-    topic_model = BERTopic.load(model_path+'bertopic_model_tokyo_olympics_tweets_unclean')
-    return topic_model
-topic_model_unclean = load_bertopic_model_unclean()
+tokyo = read_tokyo_data()
 
 @st.cache(allow_output_mutation=True)
 def load_bertopic_model():
@@ -282,14 +276,9 @@ st.write('''
     ''')
 st.write('\n')
 
-# plot topics using unclean data
-fig = LIB.visualize_barchart_titles(
-    topic_model=topic_model_unclean,
-    subplot_titles=None,
-    n_words=5,
-    top_n_topics=8,
-    height=300
-)
+# load and plot topics using unclean data
+with open('data/topics_tokyo_unclean.pickle', 'rb') as pkl:
+    fig = pickle.load(pkl)
 st.plotly_chart(fig, use_container_width=True)
 
 st.write('''
@@ -311,14 +300,9 @@ labelled_topics = [
     'Vikas Krishan (Indian Boxer)',
     ]
 
-# plot topics using clean data with stopwords removed
-fig = LIB.visualize_barchart_titles(
-    topic_model=topic_model,
-    subplot_titles=labelled_topics,
-    n_words=5,
-    top_n_topics=8,
-    height=300
-)
+# load plot topics using clean data with stopwords removed
+with open('data/topics_tokyo.pickle', 'rb') as pkl:
+    fig = pickle.load(pkl)
 st.plotly_chart(fig, use_container_width=True)
 
 st.write('''
@@ -381,7 +365,7 @@ st.write(f'''
     An an example, within the topic of 'Climate Change', we are interested in finance, politics, technology, and wildlife.  
     Using **Zero-shot Classification**, we can classify responses into one of these four categories.  
     As an added bonus, we can also find out how responders feel about the categories using **Sentiment Analysis**.  
-    We'll use a different set of 10,000 tweets related to climate change.  
+    We'll use a different set of {len(sentiment_results):,} tweets related to climate change.  
     ''')
 st.write('\n')
 
@@ -393,12 +377,12 @@ st.dataframe(sentiment_results[['Tweet']])
 def load_transfomer_pipelines():
     classifier_zero_shot = pipeline(
         task='zero-shot-classification', 
-        model=model_path+'distilbart-mnli-12-1', 
+        model='valhalla/distilbart-mnli-12-1',
         return_all_scores=True
         )
     classifier_sentiment = pipeline(
         task='sentiment-analysis', 
-        model=model_path+'distilbert-base-uncased-finetuned-sst-2-english', 
+        model = 'distilbert-base-uncased-finetuned-sst-2-english',
         return_all_scores=True
         )
     return classifier_zero_shot, classifier_sentiment

data/tokyo_topics.csv

@@ -0,0 +1,131 @@
+Topic,Count,Name
+-1,2624,-1_silver_medal_proud_mirabaichanu
+0,428,0_banda_zambia_barbra_barbra banda
+1,356,1_india_proud_indians_moment
+2,296,2_sutirtha_mukherjee_sutirtha mukherjee_tabletennis
+3,287,3_mirabaichanu hearty_lifting womens_mirabaichanu lifting_hearty congratulations
+4,248,4_race_road_road race_carapaz
+5,210,5_japan_volleyball_venezuela_flag
+6,195,6_kerr_sam_sam kerr_matildas
+7,183,7_vikas_boxing_krishan_vikas krishan
+8,163,8_gymnastics_mens gymnastics_max_whitlock
+9,148,9_tennis_murray_singles_nagal
+10,121,10_bbc_coverage_live_bbcsport
+11,120,11_ina_facebook_action_officialvkyadav
+12,115,12_puneethrajkumar cheer4india_dvitva james_dvitva_james puneethrajkumar
+13,113,13_hockey_south africa_gbhockey_africa
+14,100,14_judo_takato_gold_japans
+15,97,15_chanu_mirabai chanu_chanu wins_mirabai
+16,92,16_swimming_swimming swimming_aquatics_finals
+17,89,17_medal weightlifting_mirabaichanu winning_ace indian_congratulations ace
+18,87,18_q2_canwnt_corner_follow live
+19,85,19_winning medal_indias medal_medal india_medal
+20,84,20_basketball_3x3_3x3 basketball_usa
+21,80,21_butterfly_100m_heat_100m butterfly
+22,78,22_weightlifter_weightlifter mirabai_chanu_mirabai chanu
+23,78,23_kosovo_distria_krasniqi_distria krasniqi
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+26,71,26_ceremony_opening ceremony_opening_drones
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+30,61,30_sweden_australia_rolfo_fridolina
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+70,31,70_loving_let party_officially held_waiting gymnastics
+71,31,71_400m_mens 400m_heat_400
+72,30,72_malaysia_malaysiaboleh_malaysiaboleh congrats_malaysia malaysia
+73,30,73_time india_india clinches_clinches medal_day hearty
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+75,30,75_mirabai chanu_mirabai_chanu_saikhom mirabai
+76,30,76_football_womens football_soccer_women
+77,30,77_mcgrail_peter mcgrail_peter_butdee
+78,29,78_display weightlifting_amazing display_absolutely amazing_display
+79,29,79_cheer4india_medal cheer4india_indias mirabaichanu_medal medal
+80,29,80_mirabaichanu teamindia_teamindia_teamindia mirabaichanu_proud teamindia
+81,29,81_spain_waterpolo_water polo_polo
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+83,28,83_pen_pen swevaus_swevaus pen_swevaus
+84,28,84_mirabaichanu mirabaichanu_mirabaichanu_congratulations mirabaichanu_power
+85,27,85_congratulations mirabai_chanu winning_mirabai chanu_49 kg
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+89,27,89_potential_massive potential_long term_term quick
+90,26,90_matildas_fark_pen matildas_matildas matildas
+91,26,91_grande_carapaz_hispanos_grande carapaz
+92,25,92_gift selflove_mensfashion_selflove_selfie mensfashion
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+96,23,96_new zealand_zealand_hockey_new
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+98,23,98_weightlifting lets_lets cheer_cheer india_cheer
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+100,22,100_mirabaichanu silver_silver_silver mirabaichanu_mam silver
+101,22,101_nigeria_ghana_team_ghanas
+102,22,102_aus_ausvswe_aussies_australia
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+128,15,128_medal mirabai_kg womens_medal 49_womens weightlifting

models/bertopic_model_tokyo_olympics_tweets_unclean

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:0de856ed231c12e7baeaff15eb3159e1a5ef7c5512b459f915f46712f6d203a3
-size 71961846

models/distilbart-mnli-12-1/README.md

@@ -1,59 +0,0 @@
----
-datasets:
-- mnli
-tags:
-- distilbart
-- distilbart-mnli
-pipeline_tag: zero-shot-classification
----
-
-# DistilBart-MNLI
-
-distilbart-mnli is the distilled version of bart-large-mnli created using the **No Teacher Distillation** technique proposed for BART summarisation by Huggingface, [here](https://github.com/huggingface/transformers/tree/master/examples/seq2seq#distilbart).
-
-We just copy alternating layers from `bart-large-mnli` and finetune more on the same data. 
-
-
-|                                                                                      | matched acc | mismatched acc |
-| ------------------------------------------------------------------------------------ | ----------- | -------------- |
-| [bart-large-mnli](https://huggingface.co/facebook/bart-large-mnli) (baseline, 12-12) | 89.9        | 90.01          |
-| [distilbart-mnli-12-1](https://huggingface.co/valhalla/distilbart-mnli-12-1)         | 87.08       | 87.5           |
-| [distilbart-mnli-12-3](https://huggingface.co/valhalla/distilbart-mnli-12-3)         | 88.1        | 88.19          |
-| [distilbart-mnli-12-6](https://huggingface.co/valhalla/distilbart-mnli-12-6)         | 89.19       | 89.01          |
-| [distilbart-mnli-12-9](https://huggingface.co/valhalla/distilbart-mnli-12-9)         | 89.56       | 89.52          |
-
-
-This is a very simple and effective technique, as we can see the performance drop is very little.
-
-Detailed performace trade-offs will be posted in this [sheet](https://docs.google.com/spreadsheets/d/1dQeUvAKpScLuhDV1afaPJRRAE55s2LpIzDVA5xfqxvk/edit?usp=sharing).
-
-
-## Fine-tuning
-If you want to train these models yourself, clone the [distillbart-mnli repo](https://github.com/patil-suraj/distillbart-mnli) and follow the steps below
-
-Clone and install transformers from source
-```bash
-git clone https://github.com/huggingface/transformers.git
-pip install -qqq -U ./transformers
-```
-
-Download MNLI data
-```bash
-python transformers/utils/download_glue_data.py --data_dir glue_data --tasks MNLI
-```
-
-Create student model
-```bash
-python create_student.py \
-  --teacher_model_name_or_path facebook/bart-large-mnli \
-  --student_encoder_layers 12 \
-  --student_decoder_layers 6 \
-  --save_path student-bart-mnli-12-6 \
-```
-
-Start fine-tuning
-```bash
-python run_glue.py args.json
-```
-
-You can find the logs of these trained models in this [wandb project](https://wandb.ai/psuraj/distilbart-mnli).

models/distilbart-mnli-12-1/config.json

@@ -1,56 +0,0 @@
-{
-  "_num_labels": 3,
-  "activation_dropout": 0.0,
-  "activation_function": "gelu",
-  "add_bias_logits": false,
-  "add_final_layer_norm": false,
-  "architectures": [
-    "BartForSequenceClassification"
-  ],
-  "attention_dropout": 0.1,
-  "bos_token_id": 0,
-  "classif_dropout": 0.0,
-  "classifier_dropout": 0.0,
-  "d_model": 1024,
-  "decoder_attention_heads": 16,
-  "decoder_ffn_dim": 4096,
-  "decoder_layerdrop": 0.0,
-  "decoder_layers": 1,
-  "decoder_start_token_id": 2,
-  "dropout": 0.1,
-  "encoder_attention_heads": 16,
-  "encoder_ffn_dim": 4096,
-  "encoder_layerdrop": 0.0,
-  "encoder_layers": 12,
-  "eos_token_id": 2,
-  "extra_pos_embeddings": 2,
-  "finetuning_task": "mnli",
-  "force_bos_token_to_be_generated": false,
-  "forced_eos_token_id": 2,
-  "gradient_checkpointing": false,
-  "id2label": {
-    "0": "contradiction",
-    "1": "neutral",
-    "2": "entailment"
-  },
-  "init_std": 0.02,
-  "is_encoder_decoder": true,
-  "label2id": {
-    "contradiction": 0,
-    "entailment": 2,
-    "neutral": 1
-  },
-  "max_position_embeddings": 1024,
-  "model_type": "bart",
-  "normalize_before": false,
-  "normalize_embedding": true,
-  "num_hidden_layers": 12,
-  "output_past": false,
-  "pad_token_id": 1,
-  "scale_embedding": false,
-  "static_position_embeddings": false,
-  "total_flos": 153130534133111808,
-  "transformers_version": "4.7.0.dev0",
-  "use_cache": true,
-  "vocab_size": 50265
-}

models/distilbart-mnli-12-1/pytorch_model.bin

@@ -1,3 +0,0 @@
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-oid sha256:aa79ff59084a5036b07a9cffeaa1b1b7c1aa5edeb1885416a734c001a09aa046
-size 890410947

models/distilbart-mnli-12-1/special_tokens_map.json

@@ -1 +0,0 @@
-{"bos_token": {"content": "<s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, "eos_token": {"content": "</s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, "unk_token": {"content": "<unk>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, "sep_token": {"content": "</s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, "pad_token": {"content": "<pad>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, "cls_token": {"content": "<s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, "mask_token": {"content": "<mask>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true}}

models/distilbart-mnli-12-1/tokenizer_config.json

@@ -1 +0,0 @@
-{"model_max_length": 1024}

models/distilbert-base-uncased-finetuned-sst-2-english/README.md

@@ -1,31 +0,0 @@
----
-language: en
-license: apache-2.0
-datasets:
-- sst-2
----
-
-# DistilBERT base uncased finetuned SST-2
-
-This model is a fine-tune checkpoint of [DistilBERT-base-uncased](https://huggingface.co/distilbert-base-uncased), fine-tuned on SST-2.
-This model reaches an accuracy of 91.3 on the dev set (for comparison, Bert bert-base-uncased version reaches an accuracy of 92.7).
-
-For more details about DistilBERT, we encourage users to check out [this model card](https://huggingface.co/distilbert-base-uncased).
-
-# Fine-tuning hyper-parameters
-
-- learning_rate = 1e-5
-- batch_size = 32
-- warmup = 600
-- max_seq_length = 128
-- num_train_epochs = 3.0
-
-# Bias
-
-Based on a few experimentations, we observed that this model could produce biased predictions that target underrepresented populations.
-
-For instance, for sentences like `This film was filmed in COUNTRY`, this binary classification model will give radically different probabilities for the positive label depending on the country (0.89 if the country is France, but 0.08 if the country is Afghanistan) when nothing in the input indicates such a strong semantic shift. In this [colab](https://colab.research.google.com/gist/ageron/fb2f64fb145b4bc7c49efc97e5f114d3/biasmap.ipynb), [Aurélien Géron](https://twitter.com/aureliengeron) made an interesting map plotting these probabilities for each country.
-
-<img src="https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/map.jpeg" alt="Map of positive probabilities per country." width="500"/>
-
-We strongly advise users to thoroughly probe these aspects on their use-cases in order to evaluate the risks of this model. We recommend looking at the following bias evaluation datasets as a place to start: [WinoBias](https://huggingface.co/datasets/wino_bias), [WinoGender](https://huggingface.co/datasets/super_glue), [Stereoset](https://huggingface.co/datasets/stereoset).

models/distilbert-base-uncased-finetuned-sst-2-english/config.json

@@ -1,31 +0,0 @@
-{
-  "activation": "gelu",
-  "architectures": [
-    "DistilBertForSequenceClassification"
-  ],
-  "attention_dropout": 0.1,
-  "dim": 768,
-  "dropout": 0.1,
-  "finetuning_task": "sst-2",
-  "hidden_dim": 3072,
-  "id2label": {
-    "0": "NEGATIVE",
-    "1": "POSITIVE"
-  },
-  "initializer_range": 0.02,
-  "label2id": {
-    "NEGATIVE": 0,
-    "POSITIVE": 1
-  },
-  "max_position_embeddings": 512,
-  "model_type": "distilbert",
-  "n_heads": 12,
-  "n_layers": 6,
-  "output_past": true,
-  "pad_token_id": 0,
-  "qa_dropout": 0.1,
-  "seq_classif_dropout": 0.2,
-  "sinusoidal_pos_embds": false,
-  "tie_weights_": true,
-  "vocab_size": 30522
-}

models/distilbert-base-uncased-finetuned-sst-2-english/pytorch_model.bin

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:60554cbd7781b09d87f1ececbea8c064b94e49a7f03fd88e8775bfe6cc3d9f88
-size 267844284

models/distilbert-base-uncased-finetuned-sst-2-english/tokenizer_config.json

@@ -1 +0,0 @@
-{"model_max_length": 512, "do_lower_case": true}