# embeddings_queue

created_at: 2024-07-28 18:59:18
topic: persistent://default/default/4bfc3c11-fb71-4cc9-8c12-01ed076a0e01
id: 089a2da3-bffc-415a-9f96-988f97acd981
encoding: FLOAT32
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created_at: 2024-07-28 18:59:18
topic: persistent://default/default/4bfc3c11-fb71-4cc9-8c12-01ed076a0e01
id: e04150ea-dff3-4986-a573-d42d64036515
encoding: FLOAT32
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created_at: 2024-07-28 18:59:18
topic: persistent://default/default/4bfc3c11-fb71-4cc9-8c12-01ed076a0e01
id: 753dff6a-36bd-4ecb-abe2-91099e3a4430
encoding: FLOAT32
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created_at: 2024-07-28 18:59:18
topic: persistent://default/default/4bfc3c11-fb71-4cc9-8c12-01ed076a0e01
id: c60a0fd0-b413-4425-991c-cab9e1cf39a3
encoding: FLOAT32
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created_at: 2024-07-28 18:59:18
topic: persistent://default/default/4bfc3c11-fb71-4cc9-8c12-01ed076a0e01
id: 0a340099-627f-4aa6-9024-18561e1554b8
encoding: FLOAT32
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created_at: 2024-07-28 18:59:18
topic: persistent://default/default/4bfc3c11-fb71-4cc9-8c12-01ed076a0e01
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created_at: 2024-07-28 18:59:18
topic: persistent://default/default/4bfc3c11-fb71-4cc9-8c12-01ed076a0e01
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We'll provide the model with 5 categories and a catch-all of \"Could not classify\" for ones that it cannot place.\n\n\n```python\nzero_shot_prompt = '''You are a data expert working for the National Library of Scotland.\nYou are analysing all transactions over \u00a325,000 in value and classifying them into one of five categories.\nThe five categories are Building Improvement, Literature & Archive, Utility Bills, Professional Services and Software/IT.\nIf you can't tell what it is, say Could not classify\n\nTransaction:\n\nSupplier: SUPPLIER_NAME\nDescription: DESCRIPTION_TEXT\nValue: TRANSACTION_VALUE\n\nThe classification is:'''\n\n```\n\n\n```python\n# Get a test transaction\ntransaction = transactions.iloc[0]\n\n# Interpolate the values into the prompt\nprompt = zero_shot_prompt.replace('SUPPLIER_NAME',transaction['Supplier'])\nprompt = prompt.replace('DESCRIPTION_TEXT',transaction['Description'])\nprompt = prompt.replace('TRANSACTION_VALUE',str(transaction['Transaction value (\u00a3)']))\n\n# Use our completion function to return a prediction\ncompletion_response = request_completion(prompt)\nprint(completion_response.choices[0].text)\n\n```\n\n     Building Improvement\n\n\nOur first attempt is correct, M & J Ballantyne Ltd are a house builder and the work they performed is indeed Building Improvement.\n\nLets expand the sample size to 25 and see how it performs, again with just a simple prompt to guide it"}


created_at: 2024-07-28 18:59:18
topic: persistent://default/default/4bfc3c11-fb71-4cc9-8c12-01ed076a0e01
id: 7652d4d8-4e52-4c40-93d4-09d2d9c2608d
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\"https://github.com/openai/openai-cookbook/blob/main/examples/Multiclass_classification_for_transactions.ipynb\", \"title\": \"Multiclass Classification for Transactions\", \"tokens\": 12990, \"retrieve_doc\": false, \"source\": \"openai_cookbooks\"}, \"hash\": \"fc4c48b49d56595ea7ad607e14f7ef581cbedfabef444f56264294edcc0a2f22\", \"class_name\": \"RelatedNodeInfo\"}, \"2\": {\"node_id\": \"0d33e960-07af-401f-ab60-1c9e37eec2d9\", \"node_type\": \"1\", \"metadata\": {\"url\": \"https://github.com/openai/openai-cookbook/blob/main/examples/Multiclass_classification_for_transactions.ipynb\", \"title\": \"Multiclass Classification for Transactions\", \"tokens\": 12990, \"retrieve_doc\": false, \"source\": \"openai_cookbooks\"}, \"hash\": \"4a911dc26f5b7dc74dae948a37cb7e2aef7049301fd1064359dee0fd9b64d338\", \"class_name\": \"RelatedNodeInfo\"}, \"3\": {\"node_id\": \"f0eb2a92-9757-4854-ad5c-9c8489178282\", \"node_type\": \"1\", \"metadata\": {}, \"hash\": \"a7b37a180ec366ebae333ea01fbad729667d5e50d73da275472b863948435004\", \"class_name\": \"RelatedNodeInfo\"}}, \"text\": \"\", \"mimetype\": \"text/plain\", \"start_char_idx\": 5593, \"end_char_idx\": 8660, \"text_template\": \"{metadata_str}\\n\\n{content}\", \"metadata_template\": \"{key}: {value}\", \"metadata_seperator\": \"\\n\", \"class_name\": \"TextNode\"}", "_node_type": "TextNode", "document_id": "fe0da3db-4d84-4600-b39a-29c7e7a81c72", "doc_id": "fe0da3db-4d84-4600-b39a-29c7e7a81c72", "ref_doc_id": "fe0da3db-4d84-4600-b39a-29c7e7a81c72", "chroma:document": "```python\nzero_shot_prompt = '''You are a data expert working for the National Library of Scotland.\nYou are analysing all transactions over \u00a325,000 in value and classifying them into one of five categories.\nThe five categories are Building Improvement, Literature & Archive, Utility Bills, Professional Services and Software/IT.\nIf you can't tell what it is, say Could not classify\n\nTransaction:\n\nSupplier: SUPPLIER_NAME\nDescription: DESCRIPTION_TEXT\nValue: TRANSACTION_VALUE\n\nThe classification is:'''\n\n```\n\n\n```python\n# Get a test transaction\ntransaction = transactions.iloc[0]\n\n# Interpolate the values into the prompt\nprompt = zero_shot_prompt.replace('SUPPLIER_NAME',transaction['Supplier'])\nprompt = prompt.replace('DESCRIPTION_TEXT',transaction['Description'])\nprompt = prompt.replace('TRANSACTION_VALUE',str(transaction['Transaction value (\u00a3)']))\n\n# Use our completion function to return a prediction\ncompletion_response = request_completion(prompt)\nprint(completion_response.choices[0].text)\n\n```\n\n     Building Improvement\n\n\nOur first attempt is correct, M & J Ballantyne Ltd are a house builder and the work they performed is indeed Building Improvement.\n\nLets expand the sample size to 25 and see how it performs, again with just a simple prompt to guide it\n\n\n```python\ntest_transactions = transactions.iloc[:25]\ntest_transactions['Classification'] = test_transactions.apply(lambda x: classify_transaction(x,zero_shot_prompt),axis=1)\n\n```\n\n    /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n    A value is trying to be set on a copy of a slice from a DataFrame.\n    Try using .loc[row_indexer,col_indexer] = value instead\n    \n    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n      \n\n\n\n```python\ntest_transactions['Classification'].value_counts()\n\n```\n\n\n\n\n     Building Improvement    14\n     Could not classify       5\n     Literature & Archive     3\n     Software/IT              2\n     Utility Bills            1\n    Name: Classification, dtype: int64\n\n\n\n\n```python\ntest_transactions.head(25)\n\n```\n\n\n\n\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Date</th>\n      <th>Supplier</th>\n      <th>Description</th>\n      <th>Transaction value (\u00a3)</th>\n      <th>Classification</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>21/04/2016</td>\n      <td>M &amp; J Ballantyne Ltd</td>\n      <td>George IV Bridge Work</td>\n      <td>35098.0</td>\n      <td>Building Improvement</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>26/04/2016</td>\n      <td>Private Sale</td>\n      <td>Literary &amp; Archival Items</td>\n      <td>30000.0</td>\n      <td>Literature &amp;"}


created_at: 2024-07-28 18:59:18
topic: persistent://default/default/4bfc3c11-fb71-4cc9-8c12-01ed076a0e01
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created_at: 2024-07-28 18:59:18
topic: persistent://default/default/4bfc3c11-fb71-4cc9-8c12-01ed076a0e01
id: 0b51c0e9-5088-4167-9545-84f9f903e605
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created_at: 2024-07-28 18:59:18
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created_at: 2024-07-28 18:59:18
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created_at: 2024-07-28 18:59:18
topic: persistent://default/default/4bfc3c11-fb71-4cc9-8c12-01ed076a0e01
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created_at: 2024-07-28 18:59:18
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created_at: 2024-07-28 18:59:18
topic: persistent://default/default/4bfc3c11-fb71-4cc9-8c12-01ed076a0e01
id: cd61d17f-248f-481d-a630-30a08dd9e50c
encoding: FLOAT32
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The ones that it could not classify were tougher cases with few clues as to their topic, but maybe if we clean up the labelled dataset to give more examples we can get better performance.\n\n## Classification with Embeddings\n\nLets create embeddings from the small set that we've classified so far - we've made a set of labelled examples by running the zero-shot classifier on 101 transactions from our dataset and manually correcting the 15 **Could not classify** results that we got\n\n### Create embeddings\n\nThis initial section reuses the approach from the [Get_embeddings_from_dataset Notebook](Get_embeddings_from_dataset.ipynb) to create embeddings from a combined field concatenating all of our features\n\n\n```python\ndf = pd.read_csv('./data/labelled_transactions.csv')\ndf.head()\n\n```\n\n\n\n\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Date</th>\n      <th>Supplier</th>\n      <th>Description</th>\n      <th>Transaction value (\u00a3)</th>\n      <th>Classification</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>15/08/2016</td>\n      <td>Creative Video Productions Ltd</td>\n      <td>Kelvin Hall</td>\n      <td>26866</td>\n      <td>Other</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>29/05/2017</td>\n      <td>John Graham Construction Ltd</td>\n      <td>Causewayside Refurbishment</td>\n      <td>74806</td>\n      <td>Building Improvement</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>29/05/2017</td>\n      <td>Morris &amp; Spottiswood Ltd</td>\n      <td>George IV Bridge Work</td>\n      <td>56448</td>\n      <td>Building Improvement</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>31/05/2017</td>\n      <td>John Graham Construction Ltd</td>\n      <td>Causewayside Refurbishment</td>\n      <td>164691</td>\n      <td>Building Improvement</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>24/07/2017</td>\n      <td>John Graham Construction Ltd</td>\n      <td>Causewayside Refurbishment</td>\n      <td>27926</td>\n      <td>Building Improvement</td>\n    </tr>\n  </tbody>\n</table>\n</div>"}


created_at: 2024-07-28 18:59:18
topic: persistent://default/default/4bfc3c11-fb71-4cc9-8c12-01ed076a0e01
id: 677b0f6f-1fc9-48af-8d16-fe53afbbde0c
encoding: FLOAT32
metadata:
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Description: \" + df['Description'].str.strip() + \"; Value: \" + str(df['Transaction value (\u00a3)']).strip()\ndf.head(2)\n\n```\n\n\n\n\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Date</th>\n      <th>Supplier</th>\n      <th>Description</th>\n      <th>Transaction value (\u00a3)</th>\n      <th>Classification</th>\n      <th>combined</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>15/08/2016</td>\n      <td>Creative Video Productions Ltd</td>\n      <td>Kelvin Hall</td>\n      <td>26866</td>\n      <td>Other</td>\n      <td>Supplier: Creative Video Productions Ltd; Desc...</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>29/05/2017</td>\n      <td>John Graham Construction Ltd</td>\n      <td>Causewayside Refurbishment</td>\n      <td>74806</td>\n      <td>Building Improvement</td>\n      <td>Supplier: John Graham Construction Ltd; Descri...</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n\n\n\n\n```python\nfrom transformers import GPT2TokenizerFast\ntokenizer = GPT2TokenizerFast.from_pretrained(\"gpt2\")\n\ndf['n_tokens'] = df.combined.apply(lambda x: len(tokenizer.encode(x)))\nlen(df)\n\n```\n\n\n\n\n    101\n\n\n\n\n```python\nembedding_path = './data/transactions_with_embeddings_100.csv'\n\n```\n\n\n```python\nfrom utils.embeddings_utils import get_embedding\n\ndf['babbage_similarity'] = df.combined.apply(lambda x: get_embedding(x, model='gpt-4'))\ndf['babbage_search'] = df.combined.apply(lambda x: get_embedding(x, model='gpt-4'))\ndf.to_csv(embedding_path)\n\n```\n\n### Use embeddings for classification\n\nNow that we have our embeddings, let see if classifying these into the categories we've named gives us any more success.\n\nFor this we'll use a template from the [Classification_using_embeddings](Classification_using_embeddings.ipynb) notebook\n\n\n```python\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import classification_report, accuracy_score\nfrom ast import literal_eval\n\nfs_df = pd.read_csv(embedding_path)\nfs_df[\"babbage_similarity\"] = fs_df.babbage_similarity.apply(literal_eval).apply(np.array)\nfs_df.head()\n\n```\n\n\n\n\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;"}


created_at: 2024-07-28 18:59:18
topic: persistent://default/default/4bfc3c11-fb71-4cc9-8c12-01ed076a0e01
id: 82130577-447d-4c44-8e10-2cf7f5bf0861
encoding: FLOAT32
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{"url": "https://github.com/openai/openai-cookbook/blob/main/examples/Multiclass_classification_for_transactions.ipynb", "title": "Multiclass Classification for Transactions", "tokens": 12990, "retrieve_doc": false, "source": "openai_cookbooks", "_node_content": "{\"id_\": \"82130577-447d-4c44-8e10-2cf7f5bf0861\", \"embedding\": null, \"metadata\": {\"url\": \"https://github.com/openai/openai-cookbook/blob/main/examples/Multiclass_classification_for_transactions.ipynb\", \"title\": \"Multiclass Classification for Transactions\", \"tokens\": 12990, \"retrieve_doc\": false, \"source\": \"openai_cookbooks\"}, \"excluded_embed_metadata_keys\": [\"url\", \"tokens\", \"retrieve_doc\", \"source\"], \"excluded_llm_metadata_keys\": [\"title\", \"tokens\", \"retrieve_doc\", \"source\"], \"relationships\": {\"1\": {\"node_id\": \"fe0da3db-4d84-4600-b39a-29c7e7a81c72\", \"node_type\": \"4\", \"metadata\": {\"url\": \"https://github.com/openai/openai-cookbook/blob/main/examples/Multiclass_classification_for_transactions.ipynb\", \"title\": \"Multiclass Classification for Transactions\", \"tokens\": 12990, \"retrieve_doc\": false, \"source\": \"openai_cookbooks\"}, \"hash\": \"fc4c48b49d56595ea7ad607e14f7ef581cbedfabef444f56264294edcc0a2f22\", \"class_name\": \"RelatedNodeInfo\"}, \"2\": {\"node_id\": \"677b0f6f-1fc9-48af-8d16-fe53afbbde0c\", \"node_type\": \"1\", \"metadata\": {\"url\": \"https://github.com/openai/openai-cookbook/blob/main/examples/Multiclass_classification_for_transactions.ipynb\", \"title\": \"Multiclass Classification for Transactions\", \"tokens\": 12990, \"retrieve_doc\": false, \"source\": \"openai_cookbooks\"}, \"hash\": \"3243c8d9920301330bef1d1abafcf14c45947cdcb522cd33885a7b5a59fdb060\", \"class_name\": \"RelatedNodeInfo\"}, \"3\": {\"node_id\": \"446b73e1-3106-4943-83de-0edc4ad09123\", \"node_type\": \"1\", \"metadata\": {}, \"hash\": \"db42d757381d67afc29cb12bebed20e8436ecba70894528e7e681938f9606eec\", \"class_name\": \"RelatedNodeInfo\"}}, \"text\": \"\", \"mimetype\": \"text/plain\", \"start_char_idx\": null, \"end_char_idx\": null, \"text_template\": \"{metadata_str}\\n\\n{content}\", \"metadata_template\": \"{key}: {value}\", \"metadata_seperator\": \"\\n\", \"class_name\": \"TextNode\"}", "_node_type": "TextNode", "document_id": "fe0da3db-4d84-4600-b39a-29c7e7a81c72", "doc_id": "fe0da3db-4d84-4600-b39a-29c7e7a81c72", "ref_doc_id": "fe0da3db-4d84-4600-b39a-29c7e7a81c72", "chroma:document": "```python\nfrom transformers import GPT2TokenizerFast\ntokenizer = GPT2TokenizerFast.from_pretrained(\"gpt2\")\n\ndf['n_tokens'] = df.combined.apply(lambda x: len(tokenizer.encode(x)))\nlen(df)\n\n```\n\n\n\n\n    101\n\n\n\n\n```python\nembedding_path = './data/transactions_with_embeddings_100.csv'\n\n```\n\n\n```python\nfrom utils.embeddings_utils import get_embedding\n\ndf['babbage_similarity'] = df.combined.apply(lambda x: get_embedding(x, model='gpt-4'))\ndf['babbage_search'] = df.combined.apply(lambda x: get_embedding(x, model='gpt-4'))\ndf.to_csv(embedding_path)\n\n```\n\n### Use embeddings for classification\n\nNow that we have our embeddings, let see if classifying these into the categories we've named gives us any more success.\n\nFor this we'll use a template from the [Classification_using_embeddings](Classification_using_embeddings.ipynb) notebook\n\n\n```python\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import classification_report, accuracy_score\nfrom ast import literal_eval\n\nfs_df = pd.read_csv(embedding_path)\nfs_df[\"babbage_similarity\"] = fs_df.babbage_similarity.apply(literal_eval).apply(np.array)\nfs_df.head()\n\n```\n\n\n\n\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Unnamed: 0</th>\n      <th>Date</th>\n      <th>Supplier</th>\n      <th>Description</th>\n      <th>Transaction value (\u00a3)</th>\n      <th>Classification</th>\n      <th>combined</th>\n      <th>n_tokens</th>\n      <th>babbage_similarity</th>\n      <th>babbage_search</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>15/08/2016</td>\n      <td>Creative Video Productions Ltd</td>\n      <td>Kelvin Hall</td>\n      <td>26866</td>\n      <td>Other</td>\n      <td>Supplier: Creative Video Productions Ltd; Desc.</td>\n      <td>136</td>\n      <td>[-0.009802100248634815, 0.022551486268639565, .</td>\n      <td>[-0.00232666521333158, 0.019198870286345482, 0.</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>29/05/2017</td>\n      <td>John Graham Construction Ltd</td>\n      <td>Causewayside Refurbishment</td>\n      <td>74806</td>\n      <td>Building Improvement</td>\n      <td>Supplier: John Graham Construction Ltd; Descri.</td>\n      <td>140</td>\n      <td>[-0.009065819904208183, 0.012094118632376194, .</td>\n      <td>[0.005169447045773268, 0.00473341578617692,"}


created_at: 2024-07-28 18:59:18
topic: persistent://default/default/4bfc3c11-fb71-4cc9-8c12-01ed076a0e01
id: 446b73e1-3106-4943-83de-0edc4ad09123
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created_at: 2024-07-28 18:59:18
topic: persistent://default/default/4bfc3c11-fb71-4cc9-8c12-01ed076a0e01
id: 5a30e65b-efb9-4901-983c-6fdd0380792c
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created_at: 2024-07-28 18:59:18
topic: persistent://default/default/4bfc3c11-fb71-4cc9-8c12-01ed076a0e01
id: c0106133-f6a1-4199-9f80-6745d4bdd64a
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created_at: 2024-07-28 18:59:18
topic: persistent://default/default/4bfc3c11-fb71-4cc9-8c12-01ed076a0e01
id: 833bbade-d795-408d-b93c-2f164234ffa5
encoding: FLOAT32
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Use `zero_division` parameter to control this behavior.\n      _warn_prf(average, modifier, msg_start, len(result))\n    /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n      _warn_prf(average, modifier, msg_start, len(result))\n    /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n      _warn_prf(average, modifier, msg_start, len(result))\n\n\nPerformance for this model is pretty strong, so creating embeddings and using even a simpler classifier looks like an effective approach as well, with the zero-shot classifier helping us do the initial classification of the unlabelled dataset.\n\nLets take it one step further and see if a fine-tuned model trained on this same labelled datasets gives us comparable results\n\n## Fine-tuned Transaction Classification\n\nFor this use case we're going to try to improve on the few-shot classification from above by training a fine-tuned model on the same labelled set of 101 transactions and applying this fine-tuned model on group of unseen transactions\n\n### Building Fine-tuned Classifier\n\nWe'll need to do some data prep first to get our data ready. This will take the following steps:\n- First we'll list out our classes and replace them with numeric identifiers. Making the model predict a single token rather than multiple consecutive ones like 'Building Improvement' should give us better results\n- We also need to add a common prefix and suffix to each example to aid the model in making predictions - in our case our text is already started with 'Supplier' and we'll add a suffix of '\\n\\n###\\n\\n'\n- Lastly we'll aid a leading whitespace onto each of our target classes for classification, again to aid the model\n\n\n```python\nft_prep_df = fs_df.copy()\nlen(ft_prep_df)\n\n```\n\n\n\n\n    101\n\n\n\n\n```python\nft_prep_df.head()\n\n```\n\n\n\n\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;"}


created_at: 2024-07-28 18:59:18
topic: persistent://default/default/4bfc3c11-fb71-4cc9-8c12-01ed076a0e01
id: d061c796-e701-4024-949f-f4db590ccb9b
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This will take the following steps:\n- First we'll list out our classes and replace them with numeric identifiers. Making the model predict a single token rather than multiple consecutive ones like 'Building Improvement' should give us better results\n- We also need to add a common prefix and suffix to each example to aid the model in making predictions - in our case our text is already started with 'Supplier' and we'll add a suffix of '\\n\\n###\\n\\n'\n- Lastly we'll aid a leading whitespace onto each of our target classes for classification, again to aid the model\n\n\n```python\nft_prep_df = fs_df.copy()\nlen(ft_prep_df)\n\n```\n\n\n\n\n    101\n\n\n\n\n```python\nft_prep_df.head()\n\n```\n\n\n\n\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Unnamed: 0</th>\n      <th>Date</th>\n      <th>Supplier</th>\n      <th>Description</th>\n      <th>Transaction value (\u00a3)</th>\n      <th>Classification</th>\n      <th>combined</th>\n      <th>n_tokens</th>\n      <th>babbage_similarity</th>\n      <th>babbage_search</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>15/08/2016</td>\n      <td>Creative Video Productions Ltd</td>\n      <td>Kelvin Hall</td>\n      <td>26866</td>\n      <td>Other</td>\n      <td>Supplier: Creative Video Productions Ltd; Desc.</td>\n      <td>12</td>\n      <td>[-0.009630300104618073, 0.009887108579277992, .</td>\n      <td>[-0.008217384107410908, 0.025170527398586273, .</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>29/05/2017</td>\n      <td>John Graham Construction Ltd</td>\n      <td>Causewayside Refurbishment</td>\n      <td>74806</td>\n      <td>Building Improvement</td>\n      <td>Supplier: John Graham Construction Ltd; Descri.</td>\n      <td>16</td>\n      <td>[-0.006144719664007425, -0.0018709596479311585.</td>\n      <td>[-0."}


created_at: 2024-07-28 18:59:18
topic: persistent://default/default/4bfc3c11-fb71-4cc9-8c12-01ed076a0e01
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created_at: 2024-07-28 18:59:18
topic: persistent://default/default/4bfc3c11-fb71-4cc9-8c12-01ed076a0e01
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created_at: 2024-07-28 18:59:18
topic: persistent://default/default/4bfc3c11-fb71-4cc9-8c12-01ed076a0e01
id: 99386c05-d003-4991-9fef-0a09201ba091
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created_at: 2024-07-28 18:59:18
topic: persistent://default/default/4bfc3c11-fb71-4cc9-8c12-01ed076a0e01
id: 5d089d98-881d-47b7-a76d-3029b4241ed9
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created_at: 2024-07-28 18:59:18
topic: persistent://default/default/4bfc3c11-fb71-4cc9-8c12-01ed076a0e01
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created_at: 2024-07-28 18:59:18
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created_at: 2024-07-28 18:59:18
topic: persistent://default/default/4bfc3c11-fb71-4cc9-8c12-01ed076a0e01
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created_at: 2024-07-28 18:59:18
topic: persistent://default/default/4bfc3c11-fb71-4cc9-8c12-01ed076a0e01
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created_at: 2024-07-28 18:59:18
topic: persistent://default/default/4bfc3c11-fb71-4cc9-8c12-01ed076a0e01
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Description: Literary &amp; Ar...</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Supplier: Sotheby'S; Description: Literary &amp; A...</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Supplier: City Of Edinburgh Council; Descripti...</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Supplier: John Graham Construction Ltd; Descri...</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Supplier: John Graham Construction Ltd; Descri...</td>\n      <td>2</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n\n\n\n\n```python\n# This step is to remove any existing files if we've already produced training/validation sets for this classifier\n#!rm transactions_grouped*\n\n# We output our shuffled dataframe to a .jsonl file and run the prepare_data function to get us our input files\nft_df_sorted.to_json(\"transactions_grouped.jsonl\", orient='records', lines=True)\n!openai tools fine_tunes.prepare_data -f transactions_grouped.jsonl -q\n\n```\n\n\n```python\n# This functions checks that your classes all appear in both prepared files\n# If they don't, the fine-tuned model creation will fail\ncheck_finetune_classes('transactions_grouped_prepared_train.jsonl','transactions_grouped_prepared_valid.jsonl')\n\n```\n\n    31\n    8\n    All good\n\n\n\n```python\n# This step creates your model\n!openai api fine_tunes.create -t \"transactions_grouped_prepared_train.jsonl\" -v \"transactions_grouped_prepared_valid.jsonl\" --compute_classification_metrics --classification_n_classes 5 -m curie\n\n# You can use following command to get fine tuning job status and model name, replace the job name with your job\n#!openai api fine_tunes.get -i ft-YBIc01t4hxYBC7I5qhRF3Qdx\n\n```\n\n\n```python\n# Congrats, you've got a fine-tuned model!\n# Copy/paste the name provided into the variable below and we'll take it for a spin\nfine_tuned_model = 'curie:ft-personal-2022-10-20-10-42-56'\n\n```\n\n### Applying Fine-tuned Classifier\n\nNow we'll apply our classifier to see how it performs. We only had 31 unique observations in our training set and 8 in our validation set, so lets see how the performance is"}


created_at: 2024-07-28 18:59:18
topic: persistent://default/default/4bfc3c11-fb71-4cc9-8c12-01ed076a0e01
id: e3002c28-cf15-47dd-97b5-510c19fc160b
encoding: FLOAT32
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We only had 31 unique observations in our training set and 8 in our validation set, so lets see how the performance is\n\n\n```python\ntest_set = pd.read_json('transactions_grouped_prepared_valid.jsonl', lines=True)\ntest_set.head()\n\n```\n\n\n\n\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>prompt</th>\n      <th>completion</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Supplier: Wavetek Ltd; Description: Kelvin Hal...</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Supplier: ECG Facilities Service; Description:...</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Supplier: M &amp; J Ballantyne Ltd; Description: G...</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Supplier: Private Sale; Description: Literary ...</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Supplier: Ex Libris; Description: IT equipment...</td>\n      <td>3</td>\n    </tr>\n  </tbody>\n</table>\n</div>"}


