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"colab": { "base_uri": "https://localhost:8080/" }, "id": "vGFRL6OQEycq", "outputId": "174bcdb2-b040-414e-bd7c-d9eeff2aa32c" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "GPU: Tesla T4\n", "Files in /content: ['.config', 'sample_data']\n" ] } ], "source": [ "import torch, os\n", "\n", "print(\"GPU:\", torch.cuda.get_device_name(0) if torch.cuda.is_available() else \"No GPU\")\n", "print(\"Files in /content:\", os.listdir(\"/content\"))" ] }, { "cell_type": "markdown", "source": [ "We import all required libraries at the beginning to keep the notebook clean. \n", "We do not execute any logic here." ], "metadata": { "id": "ovRZFPqzHP6L" } }, { "cell_type": "code", "source": [ "# Core\n", "import os\n", "import json\n", "import random\n", "import numpy as np\n", "import pandas as pd\n", "import torch\n", "\n", "# Hugging Face / Training\n", "from datasets import Dataset\n", "from transformers import (\n", " AutoTokenizer,\n", " AutoModelForCausalLM,\n", " BitsAndBytesConfig,\n", " TrainingArguments\n", ")\n", "from peft import LoraConfig, get_peft_model\n", "!pip install -q trl\n", "from trl import SFTTrainer\n", "\n", "# Sklearn (for later evaluation if needed)\n", "from sklearn.metrics import precision_recall_fscore_support, accuracy_score\n", "from sentence_transformers import SentenceTransformer\n", "from sklearn.linear_model import LogisticRegression\n", "\n", "embedder = SentenceTransformer(\"AI-Growth-Lab/PatentSBERTa\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 745, "referenced_widgets": [ "516128aea5c74348a90bc8ea347a2b7b", "bba93c2152554f808c4250bbbae749ba", "19b19003e23c48deb85a1bcd5f745adf", "f92395392d1e4d8f96820006ce0cd67b", "e5a3b7f2dd724e40a84aaa6db74aca7c", "aa17d2c0bc1f44a88c85b0647e354c50", "72621e8e99e345ac9fc463506350aab3", "b134dd9c07f6452db10e3a27dd612d57", "f63ed8bcb52a49af8f7f9cab15724668", "6f9015c51f244d63a9ec1ae8c8663dbe", "140f419b246f4a9a9917af03af548af6", 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"stream", "name": "stdout", "text": [ "\u001b[31mERROR: Operation cancelled by user\u001b[0m\u001b[31m\n", "\u001b[0m" ] }, { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n", "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", "You will be able to reuse this secret in all of your notebooks.\n", "Please note that authentication is recommended but still optional to access public models or datasets.\n", " warnings.warn(\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "modules.json: 0%| | 0.00/229 [00:00\n", "
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doc_idtextp_greenu
4629795239961. A method of controlling an electronic circu...0.5002420.999516
4266091279451. A computer implemented method for managing ...0.5004040.999193
4335588019221. A medical system, comprising: a filtration ...0.5004210.999158
4091893527921. A vehicle lower structure comprising: a plu...0.5004510.999099
4663285298771. A method of protecting of human and animal ...0.4994930.998986
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\n", " \n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "summary": "{\n \"name\": \"top100[[\\\"doc_id\\\",\\\"text\\\",\\\"p_green\\\",\\\"u\\\"]]\",\n \"rows\": 5,\n \"fields\": [\n {\n \"column\": \"doc_id\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 404043,\n \"min\": 8529877,\n \"max\": 9523996,\n \"num_unique_values\": 5,\n \"samples\": [\n 9127945,\n 8529877,\n 8801922\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"text\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 5,\n \"samples\": [\n \"1. A computer implemented method for managing a cargo transaction of at least one cargo, comprising: transmitting consignment data to a visual cargo server; tracking cargo status data of a vehicle transporting the cargo, wherein at least some of the cargo status data comprises data collected from a GPS-enabled monitoring device; transmitting the cargo status data via a wireless network to the visual cargo server, wherein the visual cargo server integrates the consignment data and the cargo status data to produce virtual cargo data; obtaining contextual data from one or more contextual servers; transmitting the contextual data to the visual cargo server; providing context to the visual cargo data by integrating the virtual cargo data with the contextual data to produce a contextualized visual cargo data; and presenting one or more aspects of the contextualized visual cargo data to a user, wherein the one or more aspects of the contextualized visual cargo data is selected based on a role of the user and wherein the role of the user includes consignor, consignee, and transporter.\",\n \"1. A method of protecting of human and animal hair and skin from UV radiation comprising applying to the skin or hair of a human or animal in need thereof the compounds of formula wherein R R R \\u2003or unsubstituted or substituted C R R R R D is \\u2014CO\\u2014; \\u2014COO\\u2014; \\u2014S\\u2014; \\u2014SO\\u2014; \\u2014SO E is \\u2014OR G is E; C R R R R R R R\",\n \"1. A medical system, comprising: a filtration system capable of filtering a water stream; a water purification system capable of purifying said water stream in a non-batch process, comprising a microfluidic heat exchange system comprising a dwell chamber, wherein the water stream is maintained in the dwell chamber at a pasteurization temperature for a period of time effective to pasteurize the water stream; a mixing system capable of producing a stream of dialysate from mixing one or more dialysate components with the water stream in a non-batch process; and a dialyzer system, comprising\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"p_green\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.0004045393624719846,\n \"min\": 0.4994930356490869,\n \"max\": 0.5004506698142475,\n \"num_unique_values\": 5,\n \"samples\": [\n 0.5004036978398748,\n 0.4994930356490869,\n 0.500420804464904\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"u\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.00019841561637623116,\n \"min\": 0.9989860712981738,\n \"max\": 0.9995164049421883,\n \"num_unique_values\": 5,\n \"samples\": [\n 0.9991926043202504,\n 0.9989860712981738,\n 0.999158391070192\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 15 } ] }, { "cell_type": "markdown", "source": [ "We export the 100 high-risk claims into a HITL CSV with empty columns for later steps. This file is what we’ll use for QLoRA labeling and final human review." ], "metadata": { "id": "H4AATTCwPjpS" } }, { "cell_type": "code", "source": [ "hitl_out = top100[[\"doc_id\",\"text\",\"p_green\",\"u\"]].copy()\n", "hitl_out[\"ai_suggested\"] = \"\"\n", "hitl_out[\"ai_confidence\"] = \"\"\n", "hitl_out[\"ai_rationale\"] = \"\"\n", "hitl_out[\"is_green_human\"] = \"\"\n", "\n", "hitl_out.to_csv(\"/content/hitl_green_100_newsplit.csv\", index=False)\n", "print(\"Saved -> /content/hitl_green_100_newsplit.csv\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "iu46ZiSfPpIm", "outputId": "beb42785-627d-4ad1-c254-c34d0dc878e1" }, "execution_count": 16, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Saved -> /content/hitl_green_100_newsplit.csv\n" ] } ] }, { "cell_type": "markdown", "source": [ "**PART C**" ], "metadata": { "id": "_WOKnjswQr_O" } }, { "cell_type": "markdown", "source": [ "We start Part C by choosing a free LLM that fits on a T4 with 4-bit QLoRA. \n", "I’ll use `microsoft/phi-2` because it’s ungated, small enough, and stable.We prepare a small QLoRA training subset from `train_silver` so training finishes fast. I’ll start with 8,000 examples" ], "metadata": { "id": "GwpSrRnTQqoI" } }, { "cell_type": "code", "source": [ "MODEL_NAME = \"microsoft/phi-2\"\n", "\n", "train_silver = df[df[\"split\"] == \"train_silver\"][[\"text\", \"is_green_silver\"]].dropna().copy()\n", "train_silver = train_silver.sample(n=min(8000, len(train_silver)), random_state=42).reset_index(drop=True)\n", "train_silver[\"label\"] = train_silver[\"is_green_silver\"].astype(int)\n", "train_silver.head(2)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 112 }, "id": "0-M5Ig25QwwQ", "outputId": "b85a8370-c1e7-4195-d8c7-d5eac04be4ca" }, "execution_count": 17, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " text is_green_silver label\n", "0 1. A drip irrigation emitter, comprising: a pl... 1 1\n", "1 1. A microchip for use in a switch mode power ... 1 1" ], "text/html": [ "\n", "
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textis_green_silverlabel
01. A drip irrigation emitter, comprising: a pl...11
11. A microchip for use in a switch mode power ...11
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\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "variable_name": "train_silver", "summary": "{\n \"name\": \"train_silver\",\n \"rows\": 8000,\n \"fields\": [\n {\n \"column\": \"text\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 8000,\n \"samples\": [\n \"1. An exit sign lighting retrofit system for a sign with an extruded metal housing having a slide-out notch receiving an exit template comprising: a housing that includes a notch for receiving and holding an exit sign template; an isolated power supply circuit mounted in said housing electrically coupled to a plurality of LED arrays connected in series said power supply providing power to the LED arrays in such a manner that the system is characterized by an input power factor of at least 90%; wherein each LED array contains a plurality of LEDs connected in parallel; and, a battery backup electrically coupled to said power supply in an manner where said battery supplies input power to said power supply when line voltage is unavailable, and said power supply trickle charges said battery when line voltage is available; and, wherein said LED arrays are positioned in the housing to provide approximately 120 degree light spread.\",\n \"1. A sheet discharge device, comprising: a fixing device that fixes a sheet; a curl correcting device that corrects a curl of the fixed sheet; a first guiding member that guides the fixed sheet in a sheet discharging direction; a second guiding member that guides the fixed sheet in the sheet discharging direction and is arranged on a side opposite to the first guiding member; a support part that rotatably supports the first guiding member; and a discharging roller pair that discharges the fixed sheet; wherein the curl correcting device includes a correcting roller that is attached to the first guiding member, the correcting roller presses the fixed sheet in a direction apart from the first guiding member to remove the curl of the fixed sheet when the fixed sheet is held between the discharging roller pair, the correcting roller is apart from the second guiding member when the discharging roller pair is in contact with each other, and the correcting roller contacts with the second guiding member when the discharging roller pair is apart from each other.\",\n \"1. A method for managing storage cluster membership, the method comprising: accessing a power management policy for determining membership in a storage cluster, the storage cluster comprising a plurality of storage servers as members of the storage cluster, each member including a cluster interconnect adapter to communicate with other storage servers of the storage cluster, and a plurality of storage devices, wherein the plurality of storage servers manage access to the plurality of storage devices and each storage server of the plurality of storage servers can operate in a first mode and a second mode, such that power consumption of an individual storage server operating in the first mode differs from the power consumption of the individual storage server operating in the second mode; monitoring the storage cluster based on the power management policy; based on the monitoring, selecting a first storage server to transition from the first mode to the second mode; migrating resources between the selected first storage server and a second storage server in the plurality of storage servers via the respective cluster interconnect adapters of the selected first storage server and the second storage server; and transitioning the selected first storage server to the second mode.\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"is_green_silver\",\n \"properties\": {\n \"dtype\": \"int8\",\n \"num_unique_values\": 2,\n \"samples\": [\n 0,\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"label\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 0,\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 17 } ] }, { "cell_type": "markdown", "source": [ "We convert each row into an instruction-following example with a strict JSON output. This is important because the same adapter will later power our agents and must stay structured." ], "metadata": { "id": "cOOiPnlHRfqL" } }, { "cell_type": "code", "source": [ "def build_prompt(claim_text: str) -> str:\n", " return (\n", " \"We classify patent claims as GREEN (1) or NOT GREEN (0).\\n\"\n", " \"We must use ONLY the claim text and output STRICT JSON with keys:\\n\"\n", " \"label (0/1), confidence (low/medium/high), rationale (1-2 sentences), evidence (list of phrases).\\n\\n\"\n", " f\"CLAIM:\\n{claim_text}\\n\\nJSON:\"\n", " )\n", "\n", "def build_response(label: int) -> str:\n", " # Keep response short so training is fast; the model mainly learns format + patent style.\n", " conf = \"high\"\n", " return json.dumps({\n", " \"label\": int(label),\n", " \"confidence\": conf,\n", " \"rationale\": \"Based on the claim text, the described method/system relates to green technology.\",\n", " \"evidence\": []\n", " })\n", "\n", "sft_rows = []\n", "for t, y in zip(train_silver[\"text\"], train_silver[\"label\"]):\n", " sft_rows.append({\"text\": build_prompt(t) + \"\\n\" + build_response(y)})\n", "\n", "sft_df = pd.DataFrame(sft_rows)\n", "sft_df.head(1)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 81 }, "id": "rIaNooZURi-V", "outputId": "1973961b-a7e7-4c31-8565-14f61133e1cf" }, "execution_count": 18, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " text\n", "0 We classify patent claims as GREEN (1) or NOT ..." ], "text/html": [ "\n", "
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\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "variable_name": "sft_df", "summary": "{\n \"name\": \"sft_df\",\n \"rows\": 8000,\n \"fields\": [\n {\n \"column\": \"text\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 8000,\n \"samples\": [\n \"We classify patent claims as GREEN (1) or NOT GREEN (0).\\nWe must use ONLY the claim text and output STRICT JSON with keys:\\nlabel (0/1), confidence (low/medium/high), rationale (1-2 sentences), evidence (list of phrases).\\n\\nCLAIM:\\n1. An exit sign lighting retrofit system for a sign with an extruded metal housing having a slide-out notch receiving an exit template comprising: a housing that includes a notch for receiving and holding an exit sign template; an isolated power supply circuit mounted in said housing electrically coupled to a plurality of LED arrays connected in series said power supply providing power to the LED arrays in such a manner that the system is characterized by an input power factor of at least 90%; wherein each LED array contains a plurality of LEDs connected in parallel; and, a battery backup electrically coupled to said power supply in an manner where said battery supplies input power to said power supply when line voltage is unavailable, and said power supply trickle charges said battery when line voltage is available; and, wherein said LED arrays are positioned in the housing to provide approximately 120 degree light spread.\\n\\nJSON:\\n{\\\"label\\\": 1, \\\"confidence\\\": \\\"high\\\", \\\"rationale\\\": \\\"Based on the claim text, the described method/system relates to green technology.\\\", \\\"evidence\\\": []}\",\n \"We classify patent claims as GREEN (1) or NOT GREEN (0).\\nWe must use ONLY the claim text and output STRICT JSON with keys:\\nlabel (0/1), confidence (low/medium/high), rationale (1-2 sentences), evidence (list of phrases).\\n\\nCLAIM:\\n1. A sheet discharge device, comprising: a fixing device that fixes a sheet; a curl correcting device that corrects a curl of the fixed sheet; a first guiding member that guides the fixed sheet in a sheet discharging direction; a second guiding member that guides the fixed sheet in the sheet discharging direction and is arranged on a side opposite to the first guiding member; a support part that rotatably supports the first guiding member; and a discharging roller pair that discharges the fixed sheet; wherein the curl correcting device includes a correcting roller that is attached to the first guiding member, the correcting roller presses the fixed sheet in a direction apart from the first guiding member to remove the curl of the fixed sheet when the fixed sheet is held between the discharging roller pair, the correcting roller is apart from the second guiding member when the discharging roller pair is in contact with each other, and the correcting roller contacts with the second guiding member when the discharging roller pair is apart from each other.\\n\\nJSON:\\n{\\\"label\\\": 0, \\\"confidence\\\": \\\"high\\\", \\\"rationale\\\": \\\"Based on the claim text, the described method/system relates to green technology.\\\", \\\"evidence\\\": []}\",\n \"We classify patent claims as GREEN (1) or NOT GREEN (0).\\nWe must use ONLY the claim text and output STRICT JSON with keys:\\nlabel (0/1), confidence (low/medium/high), rationale (1-2 sentences), evidence (list of phrases).\\n\\nCLAIM:\\n1. A method for managing storage cluster membership, the method comprising: accessing a power management policy for determining membership in a storage cluster, the storage cluster comprising a plurality of storage servers as members of the storage cluster, each member including a cluster interconnect adapter to communicate with other storage servers of the storage cluster, and a plurality of storage devices, wherein the plurality of storage servers manage access to the plurality of storage devices and each storage server of the plurality of storage servers can operate in a first mode and a second mode, such that power consumption of an individual storage server operating in the first mode differs from the power consumption of the individual storage server operating in the second mode; monitoring the storage cluster based on the power management policy; based on the monitoring, selecting a first storage server to transition from the first mode to the second mode; migrating resources between the selected first storage server and a second storage server in the plurality of storage servers via the respective cluster interconnect adapters of the selected first storage server and the second storage server; and transitioning the selected first storage server to the second mode.\\n\\nJSON:\\n{\\\"label\\\": 1, \\\"confidence\\\": \\\"high\\\", \\\"rationale\\\": \\\"Based on the claim text, the described method/system relates to green technology.\\\", \\\"evidence\\\": []}\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 18 } ] } ] }