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In healthcare, AI algorithms are assisting doctors in diagnosing diseases more accurately and quickly by analyzing medical images and patient records. In the finance sector, machine learning models are deployed to detect fraudulent transactions and automate trading strategies. Furthermore, robotics and automation are revolutionizing manufacturing, leading to increased efficiency and reduced operational costs. Despite its incredible potential, the rise of AI also brings ethical and societal challenges, such as job displacement and the need for robust data privacy regulations. As technology continues to evolve, society must find a balance between leveraging AI's benefits and mitigating its risks.\n", + "\"\"\"\n", + "\n", + "print(\"Original Text Length:\", len(text.split()), \"words\\n\")\n", + "summary = summarizer(text, max_length=50, min_length=20, do_sample=False)\n", + "print(\"--- GENERATED SUMMARY ---\")\n", + "print(summary[0]['summary_text'])" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 477 + }, + "id": "u9R55Bpa-gIP", + "outputId": "5f772e87-35c8-4ee0-c1e4-cd15284e24b0" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "error", + "ename": "KeyError", + "evalue": "\"Unknown task summarization, available tasks are ['any-to-any', 'audio-classification', 'automatic-speech-recognition', 'depth-estimation', 'document-question-answering', 'feature-extraction', 'fill-mask', 'image-classification', 'image-feature-extraction', 'image-segmentation', 'image-text-to-text', 'image-to-image', 'keypoint-matching', 'mask-generation', 'ner', 'object-detection', 'question-answering', 'sentiment-analysis', 'table-question-answering', 'text-classification', 'text-generation', 'text-to-audio', 'text-to-speech', 'token-classification', 'video-classification', 'visual-question-answering', 'vqa', 'zero-shot-audio-classification', 'zero-shot-classification', 'zero-shot-image-classification', 'zero-shot-object-detection', 'translation_XX_to_YY']\"", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/tmp/ipykernel_950/96277130.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtransformers\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpipeline\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0msummarizer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpipeline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"summarization\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m text = \"\"\"\n\u001b[1;32m 4\u001b[0m \u001b[0mArtificial\u001b[0m \u001b[0mIntelligence\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mAI\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mrapidly\u001b[0m \u001b[0mtransforming\u001b[0m \u001b[0mmultiple\u001b[0m \u001b[0mindustries\u001b[0m \u001b[0mby\u001b[0m \u001b[0menabling\u001b[0m \u001b[0mmachines\u001b[0m \u001b[0mto\u001b[0m \u001b[0mperform\u001b[0m \u001b[0mtasks\u001b[0m \u001b[0mthat\u001b[0m \u001b[0mtypically\u001b[0m \u001b[0mrequire\u001b[0m \u001b[0mhuman\u001b[0m \u001b[0mintelligence\u001b[0m\u001b[0;34m.\u001b[0m \u001b[0mIn\u001b[0m \u001b[0mhealthcare\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mAI\u001b[0m \u001b[0malgorithms\u001b[0m \u001b[0mare\u001b[0m \u001b[0massisting\u001b[0m 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\u001b[0mautomation\u001b[0m \u001b[0mare\u001b[0m \u001b[0mrevolutionizing\u001b[0m \u001b[0mmanufacturing\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mleading\u001b[0m \u001b[0mto\u001b[0m \u001b[0mincreased\u001b[0m \u001b[0mefficiency\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mreduced\u001b[0m \u001b[0moperational\u001b[0m \u001b[0mcosts\u001b[0m\u001b[0;34m.\u001b[0m \u001b[0mDespite\u001b[0m \u001b[0mits\u001b[0m \u001b[0mincredible\u001b[0m \u001b[0mpotential\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mrise\u001b[0m \u001b[0mof\u001b[0m \u001b[0mAI\u001b[0m \u001b[0malso\u001b[0m \u001b[0mbrings\u001b[0m \u001b[0methical\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0msocietal\u001b[0m \u001b[0m...\n\u001b[1;32m 5\u001b[0m \"\"\"\n", + "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/transformers/pipelines/__init__.py\u001b[0m in \u001b[0;36mpipeline\u001b[0;34m(task, model, config, tokenizer, feature_extractor, image_processor, processor, revision, use_fast, token, device, device_map, dtype, trust_remote_code, model_kwargs, pipeline_class, **kwargs)\u001b[0m\n\u001b[1;32m 775\u001b[0m )\n\u001b[1;32m 776\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 777\u001b[0;31m \u001b[0mnormalized_task\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtargeted_task\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtask_options\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_task\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtask\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 778\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mpipeline_class\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 779\u001b[0m \u001b[0mpipeline_class\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtargeted_task\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"impl\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/transformers/pipelines/__init__.py\u001b[0m in \u001b[0;36mcheck_task\u001b[0;34m(task)\u001b[0m\n\u001b[1;32m 379\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 380\u001b[0m \"\"\"\n\u001b[0;32m--> 381\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mPIPELINE_REGISTRY\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcheck_task\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtask\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 382\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 383\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/transformers/pipelines/base.py\u001b[0m in \u001b[0;36mcheck_task\u001b[0;34m(self, task)\u001b[0m\n\u001b[1;32m 1354\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Invalid translation task {task}, use 'translation_XX_to_YY' format\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1355\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1356\u001b[0;31m raise KeyError(\n\u001b[0m\u001b[1;32m 1357\u001b[0m \u001b[0;34mf\"Unknown task {task}, available tasks are {self.get_supported_tasks() + ['translation_XX_to_YY']}\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1358\u001b[0m )\n", + "\u001b[0;31mKeyError\u001b[0m: \"Unknown task summarization, available tasks are ['any-to-any', 'audio-classification', 'automatic-speech-recognition', 'depth-estimation', 'document-question-answering', 'feature-extraction', 'fill-mask', 'image-classification', 'image-feature-extraction', 'image-segmentation', 'image-text-to-text', 'image-to-image', 'keypoint-matching', 'mask-generation', 'ner', 'object-detection', 'question-answering', 'sentiment-analysis', 'table-question-answering', 'text-classification', 'text-generation', 'text-to-audio', 'text-to-speech', 'token-classification', 'video-classification', 'visual-question-answering', 'vqa', 'zero-shot-audio-classification', 'zero-shot-classification', 'zero-shot-image-classification', 'zero-shot-object-detection', 'translation_XX_to_YY']\"" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "!pip install \"transformers<5\" torch" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 842 + }, + "id": "5jTrhKiF_RYI", + "outputId": "8f664e1f-fa98-41b7-fb49-b057a0e97a7c" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting transformers<5\n", + " Downloading transformers-4.57.6-py3-none-any.whl.metadata (43 kB)\n", + "\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/44.0 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installation: huggingface_hub 1.6.0\n", + " Uninstalling huggingface_hub-1.6.0:\n", + " Successfully uninstalled huggingface_hub-1.6.0\n", + " Attempting uninstall: transformers\n", + " Found existing installation: transformers 5.0.0\n", + " Uninstalling transformers-5.0.0:\n", + " Successfully uninstalled transformers-5.0.0\n", + "Successfully installed huggingface-hub-0.36.2 transformers-4.57.6\n" + ] + }, + { + "output_type": "display_data", + "data": { + "application/vnd.colab-display-data+json": { + "pip_warning": { + "packages": [ + "transformers" + ] + }, + "id": "cc2adbf192a641b6859719a59b2ba01b" + } + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "from transformers import pipeline\n", + "summarizer = pipeline(\"summarization\")\n", + "text = \"\"\"\n", + "Artificial Intelligence (AI) is rapidly transforming multiple industries by enabling machines to perform tasks that typically require human intelligence. In healthcare, AI algorithms are assisting doctors in diagnosing diseases more accurately and quickly by analyzing medical images and patient records. In the finance sector, machine learning models are deployed to detect fraudulent transactions and automate trading strategies. Furthermore, robotics and automation are revolutionizing manufacturing, leading to increased efficiency and reduced operational costs. Despite its incredible potential, the rise of AI also brings ethical and societal challenges, such as job displacement and the need for robust data privacy regulations. As technology continues to evolve, society must find a balance between leveraging AI's benefits and mitigating its risks.\n", + "\"\"\"\n", + "\n", + "print(\"Original Text Length:\", len(text.split()), \"words\\n\")\n", + "summary = summarizer(text, max_length=50, min_length=20, do_sample=False)\n", + "print(\"--- GENERATED SUMMARY ---\")\n", + "print(summary[0]['summary_text'])" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 472, + "referenced_widgets": [ + "d3f2d9c35be245a68064710a921c03bf", + "95d01c9521cb41088faeb381af77b9ec", + "92553330d651436d8557c01173e5078c", + "5b9c3af591fd40df9f07519743e6c517", + "5eae864537c4412c9aa6d4f6d9f32c26", + "2f050daa2f334d3bbdcf8a5887be32cc", + "c712e6e294044a008816094432c82ac2", + "1e62371c57714544a4439e37d7a7fd5e", + "a044bad85150412f8fe0bf179e57ba8f", + "f415b03922f84a4aaadc84a857a90cc0", + "838a504ce1174c9b8a2718b27d2ac566", + "b607aac6dd62437690e2747152bb3f2c", + "0cdeea47beb74d99bab278ffb96bd937", + "595e7ea23dcc46908940e67c67808303", + "c7ac8b1eccd14482ad08a964df9de43b", + "cc9b77e4c29b4313893671e1cdcd41f2", + "f6209ca0bd4c485da560191cf462dacf", + "300c0fbe64f24358983595457488c802", + "96a9324465174fb5a80a550e2ab5f202", + "8339694c21294e17afdd6f2f135265b2", + "aba05264e1474da3ab6ffa3668baac5b", + "f028bad7222c428d967f130c8eceaa48", + "f3831c847eea400482c8f73e25126f85", + "7c3b315e085c4e9ab96268f1ab0aac2e", + "27827262d936412f81f0a14fe89bddac", + "7a33718480e34f24bdb4628e2db39f29", + "d03d2dbea4c64ad8b3263cd5f7b403e5", + "7194da6a5f8245b49c9d336f608e3b67", + "c75462ab60424d8ba8cc343738efa81e", + "86bb39df3ce9475caf6486da2a050848", + "47409d1c29c94fb8aeef429196330f23", + "1344eb21a0b04a839ff2095330f12440", + "2ccf916b18be4f0b9d5634c4cec1e95a", + "161fb38739c14116b786c1812f100a56", + "91366587cb644fc28539be1afa7cf23a", + "439a17b6a456450a9bd3b0b40a0a6186", + "e0372812f62f4f1591dd0e08f88ae961", + "51119bb53cb8477d97226c2d07197b02", + "d06740361a474c81882bb52afcb21b03", + "c416e84d96864fe3be0fe18e6e78fc7b", + "fa1a2bf6366e4ad09b6ccd1b44b2e064", + "38786ce4d5504d25a33b4aa7415929a6", + "7982ecdf624f4caf98097e378bf129db", + "685a1ccf7dfc44be83f62631a9a4d127", + "cfa12b698eb743b8881d351dc2446c32", + "473613023d2a4d70bab1a48693855199", + "c15efd02595246d391d09567664a1145", + "c1c6cb0273eb4686bcd563fe0a95bdd6", + "59601e634128472aae06f72aabaf9957", + "ec521581e6da4bab9c2f82d6a473c0bd", + "da9546f4525549998a49ed62c8bb8508", + "9144ef61b66847088e5674c8b5490202", + "4ea3cdcb35b04bbc8db142bb8eb9bc97", + "673b8cdce3a249288fece6e2306aab03", + "6c7c0ef0bce7483fb3cc3dc1b389f50e", + "9272baabe2b54a15835a018bc3d8b922", + "ae8a80a0c7f94877b73c499f1b96e223", + "b1f1236a4b4d482e863d8298bdc75a05", + "ac564c3555774d7c90ccbbd65fd80f9c", + "3e497272547e4d398df2cc06343eb3da", + "e4883c4d1e994cd593f92a758bb25557", + "790189a099a1459193afda429ed90cf1", + "1f1291e2b1c94efda42ce5e034a9192f", + "baf363b8b0f4495d8fb83bb758306bfc", + "606f18270ce24e55af70ebd583aa0278", + "11bdcc4a0fe8466ab92a2b9abec04817" + ] + }, + "id": "oQqstXLF_qoO", + "outputId": "6ae787f7-602d-4ecc-c166-3bffbf95c021" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "WARNING:torchao.kernel.intmm:Warning: Detected no triton, on systems without Triton certain kernels will not work\n", + "No model was supplied, defaulted to sshleifer/distilbart-cnn-12-6 and revision a4f8f3e (https://huggingface.co/sshleifer/distilbart-cnn-12-6).\n", + "Using a pipeline without specifying a model name and revision in production is not recommended.\n", + "/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": [ + "config.json: 0.00B [00:00, ?B/s]" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "d3f2d9c35be245a68064710a921c03bf" + } + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "pytorch_model.bin: 0%| | 0.00/1.22G [00:00 Date: Tue, 24 Mar 2026 19:05:53 +0530 Subject: [PATCH 2/2] AI text summerizer - Abhilash_aiml_032 --- .../AbhilashPanda_CSEAIML_032/Enigmatsk.ipynb | 633 +++++++----------- 1 file changed, 229 insertions(+), 404 deletions(-) diff --git a/Domain/AI AND ML/Task/AbhilashPanda_CSEAIML_032/Enigmatsk.ipynb b/Domain/AI AND ML/Task/AbhilashPanda_CSEAIML_032/Enigmatsk.ipynb index d10006c..5d866ff 100644 --- a/Domain/AI AND ML/Task/AbhilashPanda_CSEAIML_032/Enigmatsk.ipynb +++ b/Domain/AI AND ML/Task/AbhilashPanda_CSEAIML_032/Enigmatsk.ipynb @@ -14,7 +14,7 @@ }, "widgets": { "application/vnd.jupyter.widget-state+json": { - "d3f2d9c35be245a68064710a921c03bf": { + "245481239c184d63804a151f1ccc43d3": { "model_module": "@jupyter-widgets/controls", "model_name": "HBoxModel", "model_module_version": "1.5.0", @@ -29,14 +29,14 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_95d01c9521cb41088faeb381af77b9ec", - "IPY_MODEL_92553330d651436d8557c01173e5078c", - "IPY_MODEL_5b9c3af591fd40df9f07519743e6c517" + "IPY_MODEL_0625572a26c647d69ee6f397aa6d0680", + "IPY_MODEL_b9409d4ee6f8493aa300939eb33ab330", + "IPY_MODEL_f869f5bf2ce7486c8752890f8648fbc8" ], - "layout": "IPY_MODEL_5eae864537c4412c9aa6d4f6d9f32c26" + "layout": "IPY_MODEL_aea7addb77a3401f87cc11a2290f0892" } }, - "95d01c9521cb41088faeb381af77b9ec": { + "0625572a26c647d69ee6f397aa6d0680": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "model_module_version": "1.5.0", @@ -51,13 +51,13 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_2f050daa2f334d3bbdcf8a5887be32cc", + "layout": "IPY_MODEL_8d5e921233224708800a7b216e875b72", "placeholder": "​", - "style": "IPY_MODEL_c712e6e294044a008816094432c82ac2", + "style": "IPY_MODEL_67a62cf1fe9140848c9ea4d85ae27a8a", "value": "config.json: " } }, - "92553330d651436d8557c01173e5078c": { + "b9409d4ee6f8493aa300939eb33ab330": { "model_module": "@jupyter-widgets/controls", "model_name": "FloatProgressModel", "model_module_version": "1.5.0", @@ -73,15 +73,15 @@ "bar_style": "success", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_1e62371c57714544a4439e37d7a7fd5e", + "layout": "IPY_MODEL_5c73da547568447aa52368a06ce1d7a0", "max": 1, "min": 0, "orientation": "horizontal", - "style": "IPY_MODEL_a044bad85150412f8fe0bf179e57ba8f", + "style": "IPY_MODEL_b2f6ffca0b2341d48dca31f17e7a6c3c", "value": 1 } }, - "5b9c3af591fd40df9f07519743e6c517": { + "f869f5bf2ce7486c8752890f8648fbc8": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "model_module_version": "1.5.0", @@ -96,13 +96,13 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_f415b03922f84a4aaadc84a857a90cc0", + "layout": "IPY_MODEL_cf595e716b3a4a238c29c383206c6ef2", "placeholder": "​", - "style": "IPY_MODEL_838a504ce1174c9b8a2718b27d2ac566", - "value": " 1.80k/? [00:00<00:00, 61.4kB/s]" + "style": "IPY_MODEL_dc986d8a3a024c729e707eae882354ed", + "value": " 1.58k/? [00:00<00:00, 40.1kB/s]" } }, - "5eae864537c4412c9aa6d4f6d9f32c26": { + "aea7addb77a3401f87cc11a2290f0892": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -154,7 +154,7 @@ "width": null } }, - "2f050daa2f334d3bbdcf8a5887be32cc": { + "8d5e921233224708800a7b216e875b72": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -206,7 +206,7 @@ "width": null } }, - 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"layout": "IPY_MODEL_606f18270ce24e55af70ebd583aa0278", + "layout": "IPY_MODEL_c853f48907b14f19939ee3ef30b24cbd", "placeholder": "​", - "style": "IPY_MODEL_11bdcc4a0fe8466ab92a2b9abec04817", - "value": " 456k/? [00:00<00:00, 3.95MB/s]" + "style": "IPY_MODEL_90885e018a83445680479db6dccffb1d", + "value": " 1.36M/? [00:00<00:00, 38.6MB/s]" } }, - "3e497272547e4d398df2cc06343eb3da": { + "3b3c1f2a90304fb7bc6fb69dc6c9c960": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -1864,7 +1864,7 @@ "width": null } }, - "e4883c4d1e994cd593f92a758bb25557": { + "845d297de2464d6292af1f916cd60348": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -1916,7 +1916,7 @@ "width": null } }, - "790189a099a1459193afda429ed90cf1": { + "48b477beec5e4c6aa96d54f6a9544065": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", @@ -1931,7 +1931,7 @@ "description_width": "" } }, - "1f1291e2b1c94efda42ce5e034a9192f": { + "7f3053b86a1c486eae2da1f1ddae7473": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -1983,7 +1983,7 @@ "width": "20px" } }, - "baf363b8b0f4495d8fb83bb758306bfc": { + "468d20ad520142f28600be9d36ce9710": { "model_module": "@jupyter-widgets/controls", "model_name": "ProgressStyleModel", "model_module_version": "1.5.0", @@ -1999,7 +1999,7 @@ "description_width": "" } }, - "606f18270ce24e55af70ebd583aa0278": { + "c853f48907b14f19939ee3ef30b24cbd": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -2051,7 +2051,7 @@ "width": null } }, - "11bdcc4a0fe8466ab92a2b9abec04817": { + "90885e018a83445680479db6dccffb1d": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", @@ -2070,187 +2070,14 @@ } }, "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "FTOuMjPGinOP", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "971d41b6-7243-464a-af63-ed913fc7a034" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Requirement already satisfied: transformers in /usr/local/lib/python3.12/dist-packages (5.0.0)\n", - "Requirement already satisfied: torch in /usr/local/lib/python3.12/dist-packages (2.10.0+cpu)\n", - "Requirement already satisfied: filelock in /usr/local/lib/python3.12/dist-packages (from transformers) (3.25.2)\n", - "Requirement already satisfied: huggingface-hub<2.0,>=1.3.0 in /usr/local/lib/python3.12/dist-packages (from transformers) (1.6.0)\n", - "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.12/dist-packages (from transformers) (2.0.2)\n", - "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.12/dist-packages (from transformers) (26.0)\n", - "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.12/dist-packages (from transformers) (6.0.3)\n", - "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.12/dist-packages (from transformers) (2025.11.3)\n", - "Requirement already satisfied: tokenizers<=0.23.0,>=0.22.0 in /usr/local/lib/python3.12/dist-packages (from transformers) (0.22.2)\n", - "Requirement already satisfied: typer-slim in /usr/local/lib/python3.12/dist-packages (from transformers) (0.24.0)\n", - "Requirement already satisfied: safetensors>=0.4.3 in /usr/local/lib/python3.12/dist-packages (from transformers) (0.7.0)\n", - "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.12/dist-packages (from transformers) (4.67.3)\n", - "Requirement already satisfied: typing-extensions>=4.10.0 in /usr/local/lib/python3.12/dist-packages (from torch) (4.15.0)\n", - "Requirement already satisfied: setuptools in /usr/local/lib/python3.12/dist-packages (from torch) (75.2.0)\n", - "Requirement already satisfied: sympy>=1.13.3 in /usr/local/lib/python3.12/dist-packages (from torch) (1.14.0)\n", - "Requirement already satisfied: networkx>=2.5.1 in /usr/local/lib/python3.12/dist-packages (from torch) (3.6.1)\n", - "Requirement already satisfied: jinja2 in /usr/local/lib/python3.12/dist-packages (from torch) (3.1.6)\n", - "Requirement already satisfied: fsspec>=0.8.5 in /usr/local/lib/python3.12/dist-packages (from torch) (2025.3.0)\n", - "Requirement already satisfied: hf-xet<2.0.0,>=1.3.2 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub<2.0,>=1.3.0->transformers) (1.4.0)\n", - "Requirement already satisfied: httpx<1,>=0.23.0 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub<2.0,>=1.3.0->transformers) (0.28.1)\n", - "Requirement already satisfied: typer in /usr/local/lib/python3.12/dist-packages (from huggingface-hub<2.0,>=1.3.0->transformers) (0.24.1)\n", - "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.12/dist-packages (from sympy>=1.13.3->torch) (1.3.0)\n", - "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.12/dist-packages (from jinja2->torch) (3.0.3)\n", - "Requirement already satisfied: anyio in /usr/local/lib/python3.12/dist-packages (from httpx<1,>=0.23.0->huggingface-hub<2.0,>=1.3.0->transformers) (4.12.1)\n", - "Requirement already satisfied: certifi in /usr/local/lib/python3.12/dist-packages (from httpx<1,>=0.23.0->huggingface-hub<2.0,>=1.3.0->transformers) (2026.2.25)\n", - "Requirement already satisfied: httpcore==1.* in /usr/local/lib/python3.12/dist-packages (from httpx<1,>=0.23.0->huggingface-hub<2.0,>=1.3.0->transformers) (1.0.9)\n", - "Requirement already satisfied: idna in /usr/local/lib/python3.12/dist-packages (from httpx<1,>=0.23.0->huggingface-hub<2.0,>=1.3.0->transformers) (3.11)\n", - "Requirement already satisfied: h11>=0.16 in /usr/local/lib/python3.12/dist-packages (from httpcore==1.*->httpx<1,>=0.23.0->huggingface-hub<2.0,>=1.3.0->transformers) (0.16.0)\n", - "Requirement already satisfied: click>=8.2.1 in /usr/local/lib/python3.12/dist-packages (from typer->huggingface-hub<2.0,>=1.3.0->transformers) (8.3.1)\n", - "Requirement already satisfied: shellingham>=1.3.0 in /usr/local/lib/python3.12/dist-packages (from typer->huggingface-hub<2.0,>=1.3.0->transformers) (1.5.4)\n", - "Requirement already satisfied: rich>=12.3.0 in /usr/local/lib/python3.12/dist-packages (from typer->huggingface-hub<2.0,>=1.3.0->transformers) (13.9.4)\n", - "Requirement already satisfied: annotated-doc>=0.0.2 in /usr/local/lib/python3.12/dist-packages (from typer->huggingface-hub<2.0,>=1.3.0->transformers) (0.0.4)\n", - "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.12/dist-packages (from rich>=12.3.0->typer->huggingface-hub<2.0,>=1.3.0->transformers) (4.0.0)\n", - "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.12/dist-packages (from rich>=12.3.0->typer->huggingface-hub<2.0,>=1.3.0->transformers) (2.19.2)\n", - "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.12/dist-packages (from markdown-it-py>=2.2.0->rich>=12.3.0->typer->huggingface-hub<2.0,>=1.3.0->transformers) (0.1.2)\n" - ] - } - ], - "source": [ - "pip install transformers torch\n" - ] - }, { "cell_type": "code", "source": [ "from transformers import pipeline\n", - "summarizer = pipeline(\"summarization\")\n", - "text = \"\"\"\n", - "Artificial Intelligence (AI) is rapidly transforming multiple industries by enabling machines to perform tasks that typically require human intelligence. In healthcare, AI algorithms are assisting doctors in diagnosing diseases more accurately and quickly by analyzing medical images and patient records. In the finance sector, machine learning models are deployed to detect fraudulent transactions and automate trading strategies. Furthermore, robotics and automation are revolutionizing manufacturing, leading to increased efficiency and reduced operational costs. Despite its incredible potential, the rise of AI also brings ethical and societal challenges, such as job displacement and the need for robust data privacy regulations. As technology continues to evolve, society must find a balance between leveraging AI's benefits and mitigating its risks.\n", - "\"\"\"\n", "\n", - "print(\"Original Text Length:\", len(text.split()), \"words\\n\")\n", - "summary = summarizer(text, max_length=50, min_length=20, do_sample=False)\n", - "print(\"--- GENERATED SUMMARY ---\")\n", - "print(summary[0]['summary_text'])" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 477 - }, - "id": "u9R55Bpa-gIP", - "outputId": "5f772e87-35c8-4ee0-c1e4-cd15284e24b0" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "error", - "ename": "KeyError", - "evalue": "\"Unknown task summarization, available tasks are ['any-to-any', 'audio-classification', 'automatic-speech-recognition', 'depth-estimation', 'document-question-answering', 'feature-extraction', 'fill-mask', 'image-classification', 'image-feature-extraction', 'image-segmentation', 'image-text-to-text', 'image-to-image', 'keypoint-matching', 'mask-generation', 'ner', 'object-detection', 'question-answering', 'sentiment-analysis', 'table-question-answering', 'text-classification', 'text-generation', 'text-to-audio', 'text-to-speech', 'token-classification', 'video-classification', 'visual-question-answering', 'vqa', 'zero-shot-audio-classification', 'zero-shot-classification', 'zero-shot-image-classification', 'zero-shot-object-detection', 'translation_XX_to_YY']\"", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m/tmp/ipykernel_950/96277130.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtransformers\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpipeline\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0msummarizer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpipeline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"summarization\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m text = \"\"\"\n\u001b[1;32m 4\u001b[0m \u001b[0mArtificial\u001b[0m \u001b[0mIntelligence\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mAI\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mrapidly\u001b[0m \u001b[0mtransforming\u001b[0m \u001b[0mmultiple\u001b[0m \u001b[0mindustries\u001b[0m \u001b[0mby\u001b[0m \u001b[0menabling\u001b[0m \u001b[0mmachines\u001b[0m \u001b[0mto\u001b[0m \u001b[0mperform\u001b[0m \u001b[0mtasks\u001b[0m \u001b[0mthat\u001b[0m \u001b[0mtypically\u001b[0m \u001b[0mrequire\u001b[0m \u001b[0mhuman\u001b[0m \u001b[0mintelligence\u001b[0m\u001b[0;34m.\u001b[0m \u001b[0mIn\u001b[0m \u001b[0mhealthcare\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mAI\u001b[0m \u001b[0malgorithms\u001b[0m \u001b[0mare\u001b[0m \u001b[0massisting\u001b[0m \u001b[0mdoctors\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdiagnosing\u001b[0m \u001b[0mdiseases\u001b[0m \u001b[0mmore\u001b[0m \u001b[0maccurately\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mquickly\u001b[0m \u001b[0mby\u001b[0m \u001b[0manalyzing\u001b[0m \u001b[0mmedical\u001b[0m \u001b[0mimages\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mpatient\u001b[0m \u001b[0mrecords\u001b[0m\u001b[0;34m.\u001b[0m \u001b[0mIn\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mfinance\u001b[0m \u001b[0msector\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmachine\u001b[0m \u001b[0mlearning\u001b[0m \u001b[0mmodels\u001b[0m \u001b[0mare\u001b[0m \u001b[0mdeployed\u001b[0m \u001b[0mto\u001b[0m \u001b[0mdetect\u001b[0m \u001b[0mfraudulent\u001b[0m \u001b[0mtransactions\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mautomate\u001b[0m \u001b[0mtrading\u001b[0m \u001b[0mstrategies\u001b[0m\u001b[0;34m.\u001b[0m \u001b[0mFurthermore\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrobotics\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mautomation\u001b[0m \u001b[0mare\u001b[0m \u001b[0mrevolutionizing\u001b[0m \u001b[0mmanufacturing\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mleading\u001b[0m \u001b[0mto\u001b[0m \u001b[0mincreased\u001b[0m \u001b[0mefficiency\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mreduced\u001b[0m \u001b[0moperational\u001b[0m \u001b[0mcosts\u001b[0m\u001b[0;34m.\u001b[0m \u001b[0mDespite\u001b[0m \u001b[0mits\u001b[0m \u001b[0mincredible\u001b[0m \u001b[0mpotential\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mrise\u001b[0m \u001b[0mof\u001b[0m \u001b[0mAI\u001b[0m \u001b[0malso\u001b[0m \u001b[0mbrings\u001b[0m \u001b[0methical\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0msocietal\u001b[0m \u001b[0m...\n\u001b[1;32m 5\u001b[0m \"\"\"\n", - "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/transformers/pipelines/__init__.py\u001b[0m in \u001b[0;36mpipeline\u001b[0;34m(task, model, config, tokenizer, feature_extractor, image_processor, processor, revision, use_fast, token, device, device_map, dtype, trust_remote_code, model_kwargs, pipeline_class, **kwargs)\u001b[0m\n\u001b[1;32m 775\u001b[0m )\n\u001b[1;32m 776\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 777\u001b[0;31m \u001b[0mnormalized_task\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtargeted_task\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtask_options\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_task\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtask\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 778\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mpipeline_class\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 779\u001b[0m \u001b[0mpipeline_class\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtargeted_task\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"impl\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/transformers/pipelines/__init__.py\u001b[0m in \u001b[0;36mcheck_task\u001b[0;34m(task)\u001b[0m\n\u001b[1;32m 379\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 380\u001b[0m \"\"\"\n\u001b[0;32m--> 381\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mPIPELINE_REGISTRY\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcheck_task\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtask\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 382\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 383\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/transformers/pipelines/base.py\u001b[0m in \u001b[0;36mcheck_task\u001b[0;34m(self, task)\u001b[0m\n\u001b[1;32m 1354\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Invalid translation task {task}, use 'translation_XX_to_YY' format\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1355\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1356\u001b[0;31m raise KeyError(\n\u001b[0m\u001b[1;32m 1357\u001b[0m \u001b[0;34mf\"Unknown task {task}, available tasks are {self.get_supported_tasks() + ['translation_XX_to_YY']}\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1358\u001b[0m )\n", - "\u001b[0;31mKeyError\u001b[0m: \"Unknown task summarization, available tasks are ['any-to-any', 'audio-classification', 'automatic-speech-recognition', 'depth-estimation', 'document-question-answering', 'feature-extraction', 'fill-mask', 'image-classification', 'image-feature-extraction', 'image-segmentation', 'image-text-to-text', 'image-to-image', 'keypoint-matching', 'mask-generation', 'ner', 'object-detection', 'question-answering', 'sentiment-analysis', 'table-question-answering', 'text-classification', 'text-generation', 'text-to-audio', 'text-to-speech', 'token-classification', 'video-classification', 'visual-question-answering', 'vqa', 'zero-shot-audio-classification', 'zero-shot-classification', 'zero-shot-image-classification', 'zero-shot-object-detection', 'translation_XX_to_YY']\"" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "!pip install \"transformers<5\" torch" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 842 - }, - "id": "5jTrhKiF_RYI", - "outputId": "8f664e1f-fa98-41b7-fb49-b057a0e97a7c" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Collecting transformers<5\n", - " Downloading transformers-4.57.6-py3-none-any.whl.metadata (43 kB)\n", - "\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/44.0 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m44.0/44.0 kB\u001b[0m \u001b[31m1.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25hRequirement already satisfied: torch in /usr/local/lib/python3.12/dist-packages (2.10.0+cpu)\n", - "Requirement already satisfied: filelock in /usr/local/lib/python3.12/dist-packages (from transformers<5) (3.25.2)\n", - "Collecting huggingface-hub<1.0,>=0.34.0 (from transformers<5)\n", - " Downloading huggingface_hub-0.36.2-py3-none-any.whl.metadata (15 kB)\n", - "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.12/dist-packages (from transformers<5) (2.0.2)\n", - 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"\u001b[?25hInstalling collected packages: huggingface-hub, transformers\n", - " Attempting uninstall: huggingface-hub\n", - " Found existing installation: huggingface_hub 1.6.0\n", - " Uninstalling huggingface_hub-1.6.0:\n", - " Successfully uninstalled huggingface_hub-1.6.0\n", - " Attempting uninstall: transformers\n", - " Found existing installation: transformers 5.0.0\n", - " Uninstalling transformers-5.0.0:\n", - " Successfully uninstalled transformers-5.0.0\n", - "Successfully installed huggingface-hub-0.36.2 transformers-4.57.6\n" - ] - }, - { - "output_type": "display_data", - "data": { - "application/vnd.colab-display-data+json": { - "pip_warning": { - "packages": [ - "transformers" - ] - }, - "id": "cc2adbf192a641b6859719a59b2ba01b" - } - }, - "metadata": {} - } - ] - }, - { - "cell_type": "code", - "source": [ - "from transformers import pipeline\n", - "summarizer = pipeline(\"summarization\")\n", + "# Explicitly defining the model to avoid task registration issues\n", + "summarizer = pipeline(\"summarization\", model=\"facebook/bart-large-cnn\")\n", + "\n", "text = \"\"\"\n", "Artificial Intelligence (AI) is rapidly transforming multiple industries by enabling machines to perform tasks that typically require human intelligence. In healthcare, AI algorithms are assisting doctors in diagnosing diseases more accurately and quickly by analyzing medical images and patient records. In the finance sector, machine learning models are deployed to detect fraudulent transactions and automate trading strategies. Furthermore, robotics and automation are revolutionizing manufacturing, leading to increased efficiency and reduced operational costs. Despite its incredible potential, the rise of AI also brings ethical and societal challenges, such as job displacement and the need for robust data privacy regulations. As technology continues to evolve, society must find a balance between leveraging AI's benefits and mitigating its risks.\n", "\"\"\"\n", @@ -2258,93 +2085,91 @@ "print(\"Original Text Length:\", len(text.split()), \"words\\n\")\n", "summary = summarizer(text, max_length=50, min_length=20, do_sample=False)\n", "print(\"--- GENERATED SUMMARY ---\")\n", - "print(summary[0]['summary_text'])" + "print(summary[0]['summary_text'])\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 472, + "height": 437, "referenced_widgets": [ - "d3f2d9c35be245a68064710a921c03bf", - "95d01c9521cb41088faeb381af77b9ec", - "92553330d651436d8557c01173e5078c", - "5b9c3af591fd40df9f07519743e6c517", - "5eae864537c4412c9aa6d4f6d9f32c26", - "2f050daa2f334d3bbdcf8a5887be32cc", - "c712e6e294044a008816094432c82ac2", - "1e62371c57714544a4439e37d7a7fd5e", - "a044bad85150412f8fe0bf179e57ba8f", - "f415b03922f84a4aaadc84a857a90cc0", - "838a504ce1174c9b8a2718b27d2ac566", - "b607aac6dd62437690e2747152bb3f2c", - "0cdeea47beb74d99bab278ffb96bd937", - "595e7ea23dcc46908940e67c67808303", - "c7ac8b1eccd14482ad08a964df9de43b", - "cc9b77e4c29b4313893671e1cdcd41f2", - "f6209ca0bd4c485da560191cf462dacf", - "300c0fbe64f24358983595457488c802", - "96a9324465174fb5a80a550e2ab5f202", - "8339694c21294e17afdd6f2f135265b2", - "aba05264e1474da3ab6ffa3668baac5b", - "f028bad7222c428d967f130c8eceaa48", - "f3831c847eea400482c8f73e25126f85", - "7c3b315e085c4e9ab96268f1ab0aac2e", - "27827262d936412f81f0a14fe89bddac", - "7a33718480e34f24bdb4628e2db39f29", - "d03d2dbea4c64ad8b3263cd5f7b403e5", - "7194da6a5f8245b49c9d336f608e3b67", - "c75462ab60424d8ba8cc343738efa81e", - "86bb39df3ce9475caf6486da2a050848", - "47409d1c29c94fb8aeef429196330f23", - "1344eb21a0b04a839ff2095330f12440", - "2ccf916b18be4f0b9d5634c4cec1e95a", - "161fb38739c14116b786c1812f100a56", - "91366587cb644fc28539be1afa7cf23a", - "439a17b6a456450a9bd3b0b40a0a6186", - "e0372812f62f4f1591dd0e08f88ae961", - "51119bb53cb8477d97226c2d07197b02", - "d06740361a474c81882bb52afcb21b03", - 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"outputId": "6ae787f7-602d-4ecc-c166-3bffbf95c021" + "outputId": "56e1bbaf-28d3-42b6-c7d8-d06e4a067189" }, - "execution_count": null, + "execution_count": 1, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "WARNING:torchao.kernel.intmm:Warning: Detected no triton, on systems without Triton certain kernels will not work\n", - "No model was supplied, defaulted to sshleifer/distilbart-cnn-12-6 and revision a4f8f3e (https://huggingface.co/sshleifer/distilbart-cnn-12-6).\n", - "Using a pipeline without specifying a model name and revision in production is not recommended.\n", "/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", @@ -2362,7 +2187,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "d3f2d9c35be245a68064710a921c03bf" + "model_id": "245481239c184d63804a151f1ccc43d3" } }, "metadata": {} @@ -2371,12 +2196,12 @@ "output_type": "display_data", "data": { "text/plain": [ - "pytorch_model.bin: 0%| | 0.00/1.22G [00:00