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"version_minor": 0, "model_id": "b4c7981357554f9a8829f45b5591c48b" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "model.safetensors: 0%| | 0.00/1.63G [00:00 best_score:\n", " best_class = cls\n", " best_score = score\n", "\n", " return best_class\n", "\n", "\n", "support_set = {\n", " \"Sports\": \"A football match was played yesterday.\",\n", " \"Technology\": \"New AI chips are becoming faster.\",\n", " \"Food\": \"This recipe requires tomatoes and spices.\"\n", "}\n", "\n", "query_text = \"Nvidia released a new graphics processor.\"\n", "\n", "predicted = one_shot_classify(query_text, support_set)\n", "\n", "print(\"\\nPredicted class:\", predicted)\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "40Xzgb8hODb9", "outputId": "b8ca6dc0-2b6a-41bb-a80f-28038162b391" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Similarity with Sports: 0.1087\n", "Similarity with Technology: 0.5019\n", "Similarity with Food: 0.0548\n", "\n", "Predicted class: Technology\n" ] } ] }, { "cell_type": "code", "source": [], "metadata": { "id": "RneHvNuWODez" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "Few-Shot Learning\n" ], "metadata": { "id": "_e4AffE6UHCD" } }, { "cell_type": "code", "source": [ "import numpy as np\n", "import openai\n", "from openai import OpenAI\n", "from sklearn.metrics.pairwise import cosine_similarity\n", "\n", "client = OpenAI(api_key=\"Your OpenAi Api Key\")\n", "\n", "def get_embedding(text):\n", " response = client.embeddings.create(\n", " model=\"text-embedding-3-small\",\n", " input=text\n", " )\n", " return np.array(response.data[0].embedding)\n", "\n", "def few_shot_classify(query, support_examples):\n", " query_emb = get_embedding(query)\n", " best_class = None\n", " best_score = -1\n", "\n", " for cls, examples in support_examples.items():\n", " example_embs = np.array([get_embedding(ex) for ex in examples])\n", " class_emb = np.mean(example_embs, axis=0)\n", " score = cosine_similarity([query_emb], [class_emb])[0][0]\n", " print(f\"Similarity with {cls}: {score:.4f}\")\n", " if score > best_score:\n", " best_class = cls\n", " best_score = score\n", "\n", " return best_class\n", "\n", "support_set = {\n", " \"Sports\": [\n", " \"A football match was played yesterday.\",\n", " \"The cricket team won the tournament.\"\n", " ],\n", " \"Technology\": [\n", " \"New AI chips are becoming faster.\",\n", " \"Quantum computing is the future.\"\n", " ],\n", " \"Food\": [\n", " \"This recipe requires tomatoes and spices.\",\n", " \"Pasta is cooked with fresh ingredients.\"\n", " ]\n", "}\n", "\n", "query_text = \"Intel announced a new AI processor.\"\n", "predicted = few_shot_classify(query_text, support_set)\n", "\n", "print(\"\\nPredicted class:\", predicted)\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Br_iL2U4UGLB", "outputId": "3edc3360-0a85-4fb4-c640-673ce13ac451" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Similarity with Sports: 0.0754\n", "Similarity with Technology: 0.5980\n", "Similarity with Food: 0.0819\n", "\n", "Predicted class: Technology\n" ] } ] }, { "cell_type": "code", "source": [], "metadata": { "id": "Zrbm3v-sUGOn" }, "execution_count": null, "outputs": [] } ] }