1,147 Youtube_GPT_3_Finetuning.ipynb
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"DataSet: ****ATIS Airline Travel Information System****\n",
"\n",
"[https://www.kaggle.com/datasets/hassanamin/atis-airlinetravelinformationsystem](https://www.kaggle.com/datasets/hassanamin/atis-airlinetravelinformationsystem)"
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"array(['atis_flight', 'atis_flight_time', 'atis_airfare', 'atis_aircraft',\n",
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"array(['atis_flight', 'atis_flight_time', 'atis_airfare', 'atis_aircraft',\n",
" 'atis_ground_service', 'atis_airport', 'atis_airline',\n",
" 'atis_distance', 'atis_abbreviation', 'atis_ground_fare',\n",
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"array(['flight', 'flight_time', 'airfare', 'aircraft', 'ground_service',\n",
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" 'quantity', 'city', 'flight_no', 'capacity', 'flight_airfare',\n",
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"flight 3666\n",
"airfare 423\n",
"ground_service 255\n",
"airline 157\n",
"abbreviation 147\n",
"aircraft 81\n",
"flight_time 54\n",
"quantity 51\n",
"flight_airfare 21\n",
"airport 20\n",
"distance 20\n",
"city 19\n",
"ground_fare 18\n",
"capacity 16\n",
"flight_no 12\n",
"meal 6\n",
"restriction 6\n",
"airline_flight_no 2\n",
"ground_service_ground_fare 1\n",
"airfare_flight_time 1\n",
"cheapest 1\n",
"aircraft_flight_flight_no 1\n",
"Name: intent, dtype: int64"
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"abbreviation 40\n",
"airfare 40\n",
"flight 40\n",
"flight_time 40\n",
"ground_service 40\n",
"Name: intent, dtype: int64"
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" text intent\n",
"0 fare code y what does that mean abbreviation\n",
"1 does dl stand for delta abbreviation\n",
"2 what does code qw mean abbreviation\n",
"3 please explain fare code f abbreviation\n",
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"sample_data['text'] = sample_data['text'] + \"\\n\\nIntent:\\n\\n\"\n",
"# sample_data['text'] = \"Classify text into on the intent: flight, ground_service, airline, aircraft, flight_time. Text: \"+sample_data['text'] + \"\\n\\nIntent:\\n\\n\"\n",
"sample_data['intent'] = \" \"+sample_data['intent'] + \" END\"\n",
"sample_data.head()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
},
"id": "OR1BJULNh29M",
"outputId": "49998e49-c377-4247-c0ea-e2c9e4c8d921"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" text intent\n",
"0 fare code y what does that mean\\n\\nIntent:\\n\\n abbreviation END\n",
"1 does dl stand for delta\\n\\nIntent:\\n\\n abbreviation END\n",
"2 what does code qw mean\\n\\nIntent:\\n\\n abbreviation END\n",
"3 please explain fare code f\\n\\nIntent:\\n\\n abbreviation END\n",
"4 what is ewr\\n\\nIntent:\\n\\n abbreviation END"
],
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
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" <th>text</th>\n",
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" <tbody>\n",
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" <td>fare code y what does that mean\\n\\nIntent:\\n\\n</td>\n",
" <td>abbreviation END</td>\n",
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" <td>abbreviation END</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>what is ewr\\n\\nIntent:\\n\\n</td>\n",
" <td>abbreviation END</td>\n",
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},
"metadata": {},
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}
]
},
{
"cell_type": "code",
"source": [
"print(sample_data['text'][0])"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "PHfA-51Vi56x",
"outputId": "520abf81-ded2-4839-9d3c-274b3fc1dbc3"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"fare code y what does that mean\n",
"\n",
"Intent:\n",
"\n",
"\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"print(sample_data['intent'][0])"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "cYo95VjijZK1",
"outputId": "46a9e64f-b0cf-4ee9-962e-0268004fab55"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
" abbreviation END\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"sample_data.columns = ['prompt','completion']"
],
"metadata": {
"id": "4twzXMkKZwUX"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"sample_data.to_json(\"intent_sample.jsonl\", orient='records', lines=True)"
],
"metadata": {
"id": "0JW51Cb7YcZO"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# sample_data.to_json(\"intent_.json\", orient='records')"
],
"metadata": {
"id": "v0Pg58AHYzXF"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"!pip install --upgrade openai"
],
"metadata": {
"id": "1jc26bsGY7V8"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"!openai tools fine_tunes.prepare_data -f intent_sample.jsonl"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "0bhyKnRuZYG9",
"outputId": "7e287cf7-1149-4273-a4ea-1be0b71427d4"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Analyzing...\n",
"\n",
"- Your file contains 200 prompt-completion pairs\n",
"- Based on your data it seems like you're trying to fine-tune a model for classification\n",
"- For classification, we recommend you try one of the faster and cheaper models, such as `ada`\n",
"- For classification, you can estimate the expected model performance by keeping a held out dataset, which is not used for training\n",
"- There are 10 duplicated prompt-completion sets. These are rows: [11, 15, 19, 24, 27, 34, 39, 129, 152, 179]\n",
"- All prompts end with suffix `\\n\\nIntent:\\n\\n`. This suffix seems very long. Consider replacing with a shorter suffix, such as `\\n\\n###\\n\\n`\n",
"\n",
"Based on the analysis we will perform the following actions:\n",
"- [Recommended] Remove 10 duplicate rows [Y/n]: Y\n",
"- [Recommended] Would you like to split into training and validation set? [Y/n]: Y\n",
"\n",
"\n",
"Your data will be written to a new JSONL file. Proceed [Y/n]: Y\n",
"\n",
"Wrote modified files to `intent_sample_prepared_train.jsonl` and `intent_sample_prepared_valid.jsonl`\n",
"Feel free to take a look!\n",
"\n",
"Now use that file when fine-tuning:\n",
"> openai api fine_tunes.create -t \"intent_sample_prepared_train.jsonl\" -v \"intent_sample_prepared_valid.jsonl\" --compute_classification_metrics --classification_n_classes 5\n",
"\n",
"After you’ve fine-tuned a model, remember that your prompt has to end with the indicator string `\\n\\nIntent:\\n\\n` for the model to start generating completions, rather than continuing with the prompt. Make sure to include `stop=[\" END\"]` so that the generated texts ends at the expected place.\n",
"Once your model starts training, it'll approximately take 6.89 minutes to train a `curie` model, and less for `ada` and `babbage`. Queue will approximately take half an hour per job ahead of you.\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"import os\n",
"os.environ['OPENAI_API_KEY'] = \"key\""
],
"metadata": {
"id": "HXH7FcA74j6q"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"!openai api fine_tunes.create -t \"intent_sample_prepared_train.jsonl\" -v \"intent_sample_prepared_valid.jsonl\" -m 'davinci'"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "cR27GSpl4lQ0",
"outputId": "b48ec37b-a4d1-414e-edca-a7444de6451c"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Found potentially duplicated files with name 'intent_sample_prepared_train.jsonl', purpose 'fine-tune' and size 17810 bytes\n",
"file-ujyQ2W8fzEBP5nKviYzjBE6d\n",
"Enter file ID to reuse an already uploaded file, or an empty string to upload this file anyway: \n",
"Upload progress: 100% 17.8k/17.8k [00:00<00:00, 29.4Mit/s]\n",
"Uploaded file from intent_sample_prepared_train.jsonl: file-qFZO4gBx4D1AfG6nXLZRPYRO\n",
"Found potentially duplicated files with name 'intent_sample_prepared_valid.jsonl', purpose 'fine-tune' and size 4643 bytes\n",
"file-KMcFoGeWhZwDVKd2rij0fW2f\n",
"Enter file ID to reuse an already uploaded file, or an empty string to upload this file anyway: \n",
"Upload progress: 100% 4.64k/4.64k [00:00<00:00, 6.03Mit/s]\n",
"Uploaded file from intent_sample_prepared_valid.jsonl: file-PoccQPUMQqex4ctcDqC95gF7\n",
"Created fine-tune: ft-L68gCxl9xH1Cf6JZ7HYGevDV\n",
"Streaming events until fine-tuning is complete...\n",
"\n",
"(Ctrl-C will interrupt the stream, but not cancel the fine-tune)\n",
"[2022-09-11 18:52:30] Created fine-tune: ft-L68gCxl9xH1Cf6JZ7HYGevDV\n",
"[2022-09-11 18:52:34] Fine-tune costs $0.40\n",
"[2022-09-11 18:52:35] Fine-tune enqueued. Queue number: 0\n",
"[2022-09-11 18:52:36] Fine-tune started\n",
"\n",
"Stream interrupted (client disconnected).\n",
"To resume the stream, run:\n",
"\n",
" openai api fine_tunes.follow -i ft-L68gCxl9xH1Cf6JZ7HYGevDV\n",
"\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"!openai api fine_tunes.follow -i ft-L68gCxl9xH1Cf6JZ7HYGevDV"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "r3Qa8OXw759G",
"outputId": "ec455502-201b-4f79-f1ca-bc1a079d8f4b"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[2022-09-11 18:52:30] Created fine-tune: ft-L68gCxl9xH1Cf6JZ7HYGevDV\n",
"[2022-09-11 18:52:34] Fine-tune costs $0.40\n",
"[2022-09-11 18:52:35] Fine-tune enqueued. Queue number: 0\n",
"[2022-09-11 18:52:36] Fine-tune started\n",
"[2022-09-11 18:59:59] Completed epoch 1/4\n",
"[2022-09-11 19:00:46] Completed epoch 2/4\n",
"[2022-09-11 19:01:36] Completed epoch 3/4\n",
"[2022-09-11 19:02:23] Completed epoch 4/4\n",
"[2022-09-11 19:05:11] Uploaded model: davinci:ft-personal-2022-09-11-19-05-11\n",
"[2022-09-11 19:05:13] Uploaded result file: file-VWYf7Mgq4dL8lXEDP0u85f17\n",
"[2022-09-11 19:05:13] Fine-tune succeeded\n",
"\n",
"Job complete! Status: succeeded 🎉\n",
"Try out your fine-tuned model:\n",
"\n",
"openai api completions.create -m davinci:ft-personal-2022-09-11-19-05-11 -p <YOUR_PROMPT>\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# prompt = \"Do we have london flight on Monday\\n\\nIntent:\\n\\n\"\n",
"# prompt = \"what is the ap57 restriction\\n\\nIntent:\\n\\n\"\n",
"prompt = \"show me ground transportation in baltimore\\n\\nIntent:\\n\\n\""
],
"metadata": {
"id": "ev9FUSJEgGQA"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [],
"metadata": {
"id": "lC1qLV9Vwdvi"
}
},
{
"cell_type": "code",
"source": [
"import openai\n",
"openai.api_key ='key'\n",
"response = openai.Completion.create(\n",
" model=\"davinci:ft-personal-2022-09-11-19-05-11\",\n",
" prompt=prompt,\n",
" max_tokens=5,\n",
" temperature=0,\n",
" top_p=1,\n",
" frequency_penalty=0,\n",
" presence_penalty=0,\n",
" stop=[\" END\"]\n",
")\n",
"print(response['choices'][0]['text'])"
],
"metadata": {
"id": "nKlaspj-fFbm",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "d32648a5-5e10-4472-cebe-d5288107bbe7"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
" ground_service\n"
]
}
]
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "Jt22aIK29d_Z"
},
"execution_count": null,
"outputs": []
}
]
}