first commit

.gitignore

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+data/docs.csv

README.md

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+---
+title: Persplain
+emoji: 📊
+colorFrom: gray
+colorTo: green
+sdk: streamlit
+sdk_version: 1.17.0
+app_file: app.py
+pinned: false
+---
+
+# persplain
+Explainable personality recognition using interpretable transformer learning and traditional machine learning models.

app.py

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+from transformers import RobertaForSequenceClassification, RobertaTokenizer
+from simpletransformers.classification import MultiLabelClassificationModel
+from transformers_interpret import MultiLabelClassificationExplainer
+import streamlit as st
+import pandas as pd
+import numpy as np
+
+traits = ["Openness to Experience", "Conscientiousness", "Extraversion", "Agreeableness", "Neuroticism" ]
+
+short_traits = ["o", "c", "e", "a", "n"]
+
+@st.cache_resource
+def load_explainer():
+
+    print("Loading model...")
+
+    tokenizer = RobertaTokenizer.from_pretrained("andaqu/roBERTa-pers")
+    model = RobertaForSequenceClassification.from_pretrained("andaqu/roBERTa-pers", problem_type="multi_label_classification")
+
+    explainer = MultiLabelClassificationExplainer(model, tokenizer)
+
+    try:
+        model.to('cuda')
+        if next(model.parameters()).is_cuda:
+            print("Using GPU for inference!")
+    except:
+        print("GPU not available, using CPU instead.")
+
+    print("Model loaded!")
+
+    return explainer
+
+@st.cache_data
+def explain(text, _explainer):
+    
+    attributions = _explainer(text)
+
+    preds = {label: pred_prob.item() for pred_prob, label in zip(_explainer.pred_probs_list, _explainer.labels)}
+
+    attributions_html = {trait : attributions_to_html(attributions[trait]) for trait in attributions}
+
+    return {"preds": preds, "word_attributions_html": attributions_html }
+
+def attributions_to_html(attributions):
+  html = ""
+  for word, attr in attributions:
+      
+      if word in ["<s>", "</s>"]:
+        continue
+      
+      attr = round(attr, 2)
+      abs_attr = abs(attr)
+
+      if attr > 0: color = f"rgba(0,255,0,{abs_attr})"
+      elif attr < 0: color = f"rgba(255,0,0,{abs_attr})"
+      
+      html += f'<span style="background-color: {color}" title="{str(attr)}">{word}</span> '
+  
+  return html
+
+
+if "text" in st.session_state: text = st.session_state.text
+else: st.session_state.text = ""
+
+if "explanation" in st.session_state: preds = st.session_state.explanation
+else: st.session_state.explanation = {"preds": {}, "word_attributions_html": ""}
+
+
+def main():
+
+    st.title("Text to Personality Explainer 📊")
+    text = ""
+
+    explainer = load_explainer()
+
+    text = st.text_area(label="Input text here...", value="I really enjoying meeting people and working hard!")
+
+    show_prediction = st.button("Predict Traits")
+
+    st.session_state.text = text
+
+    if show_prediction and st.session_state.text:
+            
+            explanation = explain(text, explainer)
+            st.session_state.explanation = explanation
+
+    if st.session_state.text:
+
+        st.write("## Predicted Traits")
+
+        prediction = ["YES" if st.session_state.explanation["preds"][x] > 0.5 else "NO" for x in st.session_state.explanation["preds"]]
+        probability = [str(round(st.session_state.explanation["preds"][x]*100)) + "%" for x in st.session_state.explanation["preds"]]
+        
+        st.table(pd.DataFrame([prediction, probability], columns=traits, index=["Predicted Traits", "Probability"]))
+
+        st.write("## Explanation")
+        # Show five buttons, horizontally, one for each trait
+        cols = st.columns(5)
+
+        for i in range(5):
+            button = cols[i].button(traits[i])
+        
+            if button: st.markdown(st.session_state.explanation["word_attributions_html"][short_traits[i]], unsafe_allow_html=True)
+
+
+
+if __name__ == "__main__":
+    main()

notebook.ipynb

@@ -0,0 +1 @@
+{"cells":[{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["from simpletransformers.classification import MultiLabelClassificationModel\n","from transformers import RobertaForSequenceClassification, RobertaTokenizer\n","from transformers_interpret import MultiLabelClassificationExplainer\n","from sklearn.model_selection import train_test_split\n","import pandas as pd"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"executionInfo":{"elapsed":7837,"status":"ok","timestamp":1679919509858,"user":{"displayName":"Andrew Aquilina","userId":"02621426390012747312"},"user_tz":-120},"id":"jMuEYLTAVPCS","outputId":"d5307f50-00cc-4fb9-aa62-e20b7cf822a7"},"outputs":[],"source":["# Ensure GPU is available for use\n","import tensorflow as tf\n","tf.test.gpu_device_name()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":24877,"status":"ok","timestamp":1679919677949,"user":{"displayName":"Andrew Aquilina","userId":"02621426390012747312"},"user_tz":-120},"id":"MiDtoTxsQ98R","outputId":"f83e3973-e64c-4e55-8737-75b34ac800f9"},"outputs":[],"source":["# Install required libraries\n","!pip install simpletransformers\n","!pip install transformers\n","!pip install transformers_interpret"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2632,"status":"ok","timestamp":1679920350877,"user":{"displayName":"Andrew Aquilina","userId":"02621426390012747312"},"user_tz":-120},"id":"y0sqc8wfRrIH","outputId":"cb710721-280f-46db-94d6-0561f36cd3d4"},"outputs":[],"source":["# If using Google Colab, mount drive\n","try:\n","  from google.colab import drive\n","  drive.mount(\"/content/gdrive\")\n","  %cd \"/content/gdrive/MyDrive/persplain\"\n","except:\n","  print(\"Notebook is not being run from Google Colab\")\n","  %cd .."]},{"cell_type":"code","execution_count":null,"metadata":{"executionInfo":{"elapsed":560,"status":"ok","timestamp":1679920391935,"user":{"displayName":"Andrew Aquilina","userId":"02621426390012747312"},"user_tz":-120},"id":"0-RpEPVvStk2"},"outputs":[],"source":["# Prepare labels and drop unnecessary cols\n","docs = pd.read_csv('data/docs.csv', sep=',')\n","docs['labels'] = list(zip(docs.o.tolist(), docs.c.tolist(), docs.e.tolist(), docs.a.tolist(),  docs.n.tolist()))\n","docs = docs.drop([\"o\", \"c\", \"e\", \"a\", \"n\"], axis=1)"]},{"cell_type":"code","execution_count":null,"metadata":{"executionInfo":{"elapsed":827,"status":"ok","timestamp":1679919581157,"user":{"displayName":"Andrew Aquilina","userId":"02621426390012747312"},"user_tz":-120},"id":"XIRCbOWWU_-M"},"outputs":[],"source":["# Split train and test data\n","train_df, eval_df = train_test_split(docs, test_size=0.2)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":427,"referenced_widgets":["f0ed8fdee961409ea8a68e6a84b66f14","fe71030a535a43bba116498ca2459257","9fc0c79551d243c5b0d842100f32f4d5","3e58b96773c542a28ea0c294c565ba89","34499f193ab44533a319db8cbd119ea8","af3a7c669655451884576cc48e56a684","81dd1cc5c8744b30aff0e6f164701345","3490cdb4b2844c188ce789ac3769698c","7fd8daf7f5414885a6342ffccdb557f2","4bb0f5eaab5044dd89a90ea6643e49d5","8f42989efebd48b49e7d4d5f4a792460","542fe3370a87433f80296ae8ef88c7c1","e3baf965c6d64d2e8a4d2b615772ef15","c7880d3980e74e95814303303cdeeca3","f96d9ee71f4e40dbb11f5e0ce87ef93a","028aabed4f42403b9991c9dec0e8f2f3","ef681e57bc05409aa3de8e4d6f2e3ae9","a393ca3200a24491a5c382b38e648e20","d3d1bc02ce6948bb87dfd3eac935ebc6","f36a9279c9e2431489abfab37ce48889","d770bc3a56ff4939b51ce8426fa74af5","861752656acd4281b7eddf42a7b75337"]},"executionInfo":{"elapsed":1695,"status":"error","timestamp":1679901730733,"user":{"displayName":"Andrew Aquilina","userId":"02621426390012747312"},"user_tz":-120},"id":"cUuN_sXQVCba","outputId":"d3ade19e-316b-4a8d-b8ef-5b868a644dae"},"outputs":[],"source":["# Define early stopping rules\n","early_stopping = {\n","    \"early_stopping_metric\": \"eval_loss\",\n","    \"early_stopping_metric_minimize\": True,\n","    \"early_stopping_patience\": 3,\n","}\n","\n","# Initialize the model\n","model_simple = MultiLabelClassificationModel(\n","    'roberta', \n","    'roberta-base', \n","    num_labels=5, \n","    args={\n","        'train_batch_size': 2, \n","        'gradient_accumulation_steps': 16, \n","        'learning_rate': 3e-5, \n","        'num_train_epochs': 99, \n","        'max_seq_length': 512,\n","        'output_dir': './models/ov/outputs/',\n","        'early_stopping_rule': early_stopping,\n","    },\n",")\n","\n","# Train the model\n","model_simple.train_model(\n","    train_df, \n","    eval_df=eval_df,\n","    args={\n","        'overwrite_output_dir': True,\n","        'use_early_stopping': True,\n","        'early_stopping_delta': 0.01,\n","    },\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"omL-HHgOEjx3"},"outputs":[],"source":["# If not training, load the \"simple\" trained version of the model and evaluate it\n","model_simple = MultiLabelClassificationModel(\"roberta\", \"models/ov/outputs/checkpoint-1909-epoch-23\", use_cuda=False)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":4371,"status":"ok","timestamp":1679920072900,"user":{"displayName":"Andrew Aquilina","userId":"02621426390012747312"},"user_tz":-120},"id":"WFXjq2qM966S","outputId":"21f42bb0-3d0c-4d8e-9fc3-9b1672d6cc06"},"outputs":[],"source":["# Load trained model\n","model_path = \"models/ov/outputs/checkpoint-1909-epoch-23\"\n","tokenizer = RobertaTokenizer.from_pretrained(model_path)\n","model = RobertaForSequenceClassification.from_pretrained(model_path)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":33,"status":"ok","timestamp":1679920072902,"user":{"displayName":"Andrew Aquilina","userId":"02621426390012747312"},"user_tz":-120},"id":"m6nZUVGA5o5C","outputId":"4be8264a-4345-45fd-ada7-db20225cfaf2"},"outputs":[],"source":["# Make sure model uses GPU, otherwise interpreting it will be too slow\n","model.to('cuda')\n","next(model.parameters()).is_cuda"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":872,"status":"ok","timestamp":1679920089653,"user":{"displayName":"Andrew Aquilina","userId":"02621426390012747312"},"user_tz":-120},"id":"DFNXxJw2zh0l","outputId":"ea5bab6f-b88a-47f5-dae2-418343028d2c"},"outputs":[],"source":["# Initialise the explainer and make sure it is using the GPU\n","cls_explainer = MultiLabelClassificationExplainer(model, tokenizer)\n","cls_explainer.device.type == \"cuda\""]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":98,"referenced_widgets":["94ff14e2503c498aaffefa2035dcaf79","f21e1d41e9ea462e98b0531facb45f86","d4e714a1c56b4828b392452dddbdaeb6","ae483bd32b67443cb1e7c23b8fa02425","7ea3f565408044e28c177acb578881f9","2e4dcfc5802a48fd8c171b955a32b1b7","36a1f25b8b9a4173ada59010212e882a","b369691c0bee45f99bfca9ba22c43fc0","8ca1fcc60acb4a15ae7873df0972ba76","5f692fe995f143dc82686f257aab700c","2f1823ac58fe456693acc31f41ca56d1","10452f1eeef740f98fcb61291b0efff6","9faa77d090c34418af8a39b2ed25a2c5","fbcca0b70bfd49149f2df4e10a11bf67","d2a13c0168624629a63818ff2fdc4a7f","33c1c4c21b3344c4b9ebd8559a07e021","4b93b50061504f8687a5e6fdfd77a24a","4a33c0747b9e47e5a0a0c2dbc301cc81","d72916d520e5468c9c44a03dcab4a3f5","d70339cbe1314a3784a5baa54055c209","b0da721c77b04719a9f5977846664705","bc52c7f975164131a3423c177652e4bc"]},"executionInfo":{"elapsed":1937,"status":"ok","timestamp":1679920687527,"user":{"displayName":"Andrew Aquilina","userId":"02621426390012747312"},"user_tz":-120},"id":"cKFnU3OL-Zca","outputId":"8bbf3b6f-28a1-460f-edd8-4cf00ef23b26"},"outputs":[],"source":["# Predict personality from text and gather explaination\n","text = \"I like to talk to people and work hard\"\n","prediction = model_simple.predict([text])\n","word_attributions = cls_explainer(text)\n","\n","print(prediction)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":425},"executionInfo":{"elapsed":17,"status":"ok","timestamp":1679920151127,"user":{"displayName":"Andrew Aquilina","userId":"02621426390012747312"},"user_tz":-120},"id":"8qaF-xaMlyIT","outputId":"46f73f87-d2e5-4a31-94db-c0898e6505c8"},"outputs":[],"source":["# Visualise explanation\n","cls_explainer.visualize()"]}],"metadata":{"accelerator":"GPU","colab":{"provenance":[]},"gpuClass":"standard","kernelspec":{"display_name":"Python 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requirements.txt

@@ -0,0 +1,6 @@
+numpy==1.22.0
+pandas==1.4.2
+simpletransformers==0.63.9
+transformers==4.27.3
+transformers_interpret==0.10.0
+streamlit