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systems.\",\n", " \"Natural language processing tasks have benefited greatly from pre-trained language models like BERT.\",\n", " \"Machine learning techniques have been applied to various problems in bioinformatics, including gene prediction.\",\n", " \"Recent advancements in reinforcement learning have opened new avenues in artificial intelligence research.\",\n", " \"Quantum computing is a rapidly growing field with potential applications in cryptography and material science.\"\n", "]" ] }, { "cell_type": "code", "source": [ "import torch\n", "from transformers import AutoModel, AutoTokenizer\n", "from sklearn.metrics.pairwise import cosine_similarity\n", "import numpy as np" ], "metadata": { "id": "4hNYliISEui3" }, "execution_count": 2, "outputs": [] }, { "cell_type": "code", "source": [ "# Load SciBERT model and tokenizer\n", "model_name = \"allenai/scibert_scivocab_uncased\"\n", "tokenizer = AutoTokenizer.from_pretrained(model_name)\n", "model = AutoModel.from_pretrained(model_name)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 242, "referenced_widgets": [ "885937018a634de0a6deb830bb194716", "2483b50c7f9342faa91062f6dac2f2a8", "64a96102ed7b4ab0b59b0b1d97add383", "986a7cf0b85b4cc7803a03b4373edcaf", "d1b7ccd4754744509c3e45db8d651e5d", "0c1c8ec0ed6445b48f3f8787ac66ffe2", "117698ddf34d4592ad0309fa403f6122", "16774c5f05ca45d790eb770a2f1d2ef3", "1a2e33ca554e4e1c9daee3c9d9568604", "6aad8d81d1964d31babe734f809aca79", "a5f0667376174a3fad64da397a3dd92f", "b4309550192c49019293c1d1ce0f97d4", "79868f65f0ad4ffc8384e8de8f79bf0c", "50959c940ac44e23b6fc8db4d0021bc0", "e9d4ada4ded3484f88f9d41ebc9d1994", "10f4d53cfc8544b7a3a43d0a766ac18c", "c739b23d114f422cb34b0d391f80ee12", "350fb7675a154f8da40406da0271ab4d", "2158fcf3bfd543c193fa439198ce8153", "ffa1c39e07a64dff8ba37d5b6116c3a8", "82e2ec198bef407db007a5ddac81c079", "efd1dc15513f4227b37c471aac422104", "0a2021d1809a45e3ab98a356b570ce8b", 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still optional to access public models or datasets.\n", " warnings.warn(\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "config.json: 0%| | 0.00/385 [00:00