Datasets
Models
Downloads: 1
84315 lines (84315 with data), 3.1 MB
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"source": [
"# ! pip install transformers datasets\n",
"# from transformers import pipeline"
]
},
{
"cell_type": "code",
"source": [
"# Three Different NLP Tasks using 10 Different Hugging Face Models Each\n",
"# Based on Nielsen, N., How to Use Pretrained Models from Hugging Face in a Few Lines of Code https://www.youtube.com/watch?v=ntz160EnWIc\n",
"# Based on Hugging Face \"Pipelines\" Documentation https://huggingface.co/docs/transformers/en/main_classes/pipelines"
],
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"\n",
"\n",
"\n",
"# Sentiment Analysis using 10 Different Hugging Face LLMs\n",
"# Rankings based on two positive labels with highest scores\n",
"\n",
"\n",
"\n",
"# 01 distilbert/distilbert-base-uncased-finetuned-sst-2-english\n",
"# 02 RecordedFuture/Swedish-Sentiment-Fear\n",
"# 03 siebert/sentiment-roberta-large-english\n",
"# 04 SchuylerH/bert-multilingual-go-emtions\n",
"# 05 finiteautomata/bertweet-base-sentiment-analysis\n",
"# 06 pysentimiento/robertuito-sentiment-analysis\n",
"# 07 lxyuan/distilbert-base-multilingual-cased-sentiments-student\n",
"# 08 nlptown/bert-base-multilingual-uncased-sentiment\n",
"# 09 yiyanghkust/finbert-tone\n",
"# 10 StephanAkkerman/FinTwitBERT-sentiment\n",
"\n",
"\n"
],
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"classifier = pipeline(\"sentiment-analysis\", model = \"distilbert/distilbert-base-uncased-finetuned-sst-2-english\")\n",
"\n",
"# Modify text for sentiment analysis here\n",
"results = classifier([\"Large Language Models are really awesome!\", \"Medical Generative AI is very practical!\"])\n",
"for result in results:\n",
" print(f\"label: {result['label']}, with score: {round(result['score'] * 100, 4)}%\")"
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"/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: 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",
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]
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"# Modify text for sentiment analysis here\n",
"results = classifier([\"Large Language Models are really awesome!\", \"Medical Generative AI is very practical!\"])\n",
"for result in results:\n",
" print(f\"label: {result['label']}, with score: {round(result['score'] * 100, 4)}%\")"
],
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"\n",
"# Modify text for sentiment analysis here\n",
"results = classifier([\"Large Language Models are really awesome!\", \"Medical Generative AI is very practical!\"])\n",
"for result in results:\n",
" print(f\"label: {result['label']}, with score: {round(result['score'] * 100, 4)}%\")"
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"# Modify text for sentiment analysis here\n",
"results = classifier([\"Large Language Models are really awesome!\", \"Medical Generative AI is very practical!\"])\n",
"for result in results:\n",
" print(f\"label: {result['label']}, with score: {round(result['score'] * 100, 4)}%\")"
],
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"emoji is not installed, thus not converting emoticons or emojis into text. Install emoji: pip3 install emoji==0.6.0\n"
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"label: POS, with score: 99.2033%\n",
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"# Modify text for sentiment analysis here\n",
"results = classifier([\"Large Language Models are really awesome!\", \"Medical Generative AI is very practical!\"])\n",
"for result in results:\n",
" print(f\"label: {result['label']}, with score: {round(result['score'] * 100, 4)}%\")"
],
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"# Modify text for sentiment analysis here\n",
"results = classifier([\"Large Language Models are really awesome!\", \"Medical Generative AI is very practical!\"])\n",
"for result in results:\n",
" print(f\"label: {result['label']}, with score: {round(result['score'] * 100, 4)}%\")"
],
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"\n",
"# Modify text for sentiment analysis here\n",
"results = classifier([\"Large Language Models are really awesome!\", \"Medical Generative AI is very practical!\"])\n",
"for result in results:\n",
" print(f\"label: {result['label']}, with score: {round(result['score'] * 100, 4)}%\")"
],
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},
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{
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{
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"label: 5 stars, with score: 90.042%\n",
"label: 5 stars, with score: 63.2178%\n"
]
}
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},
{
"cell_type": "code",
"source": [
"classifier = pipeline(\"sentiment-analysis\", model = \"yiyanghkust/finbert-tone\")\n",
"\n",
"# Modify text for sentiment analysis here\n",
"results = classifier([\"Large Language Models are really awesome!\", \"Medical Generative AI is very practical!\"])\n",
"for result in results:\n",
" print(f\"label: {result['label']}, with score: {round(result['score'] * 100, 4)}%\")"
],
"metadata": {
"colab": {
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},
"id": "dpJngPFHZMR8",
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},
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{
"output_type": "display_data",
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],
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{
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{
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}
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{
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"classifier = pipeline(\"sentiment-analysis\", model = \"StephanAkkerman/FinTwitBERT-sentiment\")\n",
"\n",
"# Modify text for sentiment analysis here\n",
"results = classifier([\"Large Language Models are really awesome!\", \"Medical Generative AI is very practical!\"])\n",
"for result in results:\n",
" print(f\"label: {result['label']}, with score: {round(result['score'] * 100, 4)}%\")"
],
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"text": [
"label: BULLISH, with score: 73.9981%\n",
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]
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{
"cell_type": "code",
"source": [
"\n",
"\n",
"\n",
"# Summarization using 10 Different Hugging Face Models, many LLMs\n",
"# Rankings based on accuracy and insight of 1 statement summarization\n",
"\n",
"\n",
"\n",
"# 01 SyedShaheer/bart-large-cnn-samsum_tuned\n",
"# 02 sshleifer/distilbart-cnn-12-6\n",
"# 03 google/pegasus-xsum\n",
"# 04 philschmid/bart-large-cnn-samsum\n",
"# 05 IlyaGusev/mbart_ru_sum_gazeta\n",
"# 06 cnicu/t5-small-booksum\n",
"# 07 kabita-choudhary/finetuned-bart-for-conversation-summary\n",
"# 08 Falconsai/medical_summarization\n",
"# 09 EbanLee/kobart-summary-v3\n",
"# 10 transformer3/H2-keywordextractor\n",
"\n",
"\n"
],
"metadata": {
"id": "lDdeH8o63s2P"
},
"execution_count": 15,
"outputs": []
},
{
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}
},
{
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"classifier = pipeline(\"summarization\", model = \"SyedShaheer/bart-large-cnn-samsum_tuned\")\n",
"\n",
"# Modify text for summarization here\n",
"classifier(\"Medical Generative AI is very practical!\")"
],
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"[{'summary_text': 'Medical Generative AI is very practical and useful. It can be used to improve health and save lives in a variety of practical ways. It is a form of medical technology that improves health and saves lives. It also improves the quality of health care and improves quality of life in a number of areas.'}]"
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"[{'summary_text': ' Medical Generative AI is very practical! We are developing a medical AI that can be used in medical emergencies . We hope to use the technology to improve our lives and improve our quality of life in the future . We are looking forward to the development of medical AI as a tool to improve the quality of medical care .'}]"
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{
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{
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"text": [
"Some weights of PegasusForConditionalGeneration were not initialized from the model checkpoint at google/pegasus-xsum and are newly initialized: ['model.decoder.embed_positions.weight', 'model.encoder.embed_positions.weight']\n",
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"[{'summary_text': 'Artificial intelligence (AI) and machine learning are the future of medicine.'}]"
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{
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"You passed along `num_labels=3` with an incompatible id to label map: {'0': 'NEGATIVE', '1': 'POSITIVE'}. The number of labels wil be overwritten to 2.\n",
"You passed along `num_labels=3` with an incompatible id to label map: {'0': 'NEGATIVE', '1': 'POSITIVE'}. The number of labels wil be overwritten to 2.\n"
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"Your max_length is set to 300, but your input_length is only 24. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=12)\n"
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{
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"[{'summary_text': 'Medical Generative AI is very practical'}]"
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"Your max_length is set to 128, but your input_length is only 10. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=5)\n"
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"[{'summary_text': 'medical Generative AI, practical, easynotes, research, market overview, survey'}]"
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"source": [
"\n",
"\n",
"\n",
"# Generative AI using 10 Different Hugging Face LLMs\n",
"# Rankings based on accuracy and insight of 1 generation from a sentence fragment\n",
"\n",
"\n",
"\n",
"# 1 openai-community/gpt2-large\n",
"# 2 openai-community/gpt2-xl\n",
"# 3 alpindale/Mistral-7B-v0.2-hf\n",
"# 4 stabilityai/stable-code-3b\n",
"# 5 openai-community/gpt2-medium\n",
"# 6 mistralai/Mistral-7B-Instruct-v0.2\n",
"# 7 openai-community/gpt2\n",
"# 8 NousResearch/Hermes-2-Pro-Mistral-7B\n",
"# 9 openai-community/openai-gpt\n",
"# 10 berkeley-nest/Starling-LM-7B-alpha\n",
"\n",
"\n",
"# A ChatGPT3.5 03/31/24 query reflected the early stages of the technology, and detailed 6 areas of active Medical GenAI research - being trained on more recent data than test GPTs."
],
"metadata": {
"id": "tRW8-ixz7QB7"
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"[{'generated_text': 'Medical Generative AI is very different from the traditional AI approach. It is a system that learns from experience and adapts to the environment. It is not a system that is programmed to solve problems. It is a system that learns from its environment and'}]"
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{
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"text": [
"You set `add_prefix_space`. The tokenizer needs to be converted from the slow tokenizers\n",
"Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
"/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1178: UserWarning: Using the model-agnostic default `max_length` (=20) to control the generation length. We recommend setting `max_new_tokens` to control the maximum length of the generation.\n",
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},
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{
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"generator = pipeline(model=\"stabilityai/stable-code-3b\")\n",
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"[{'generated_text': 'Medical Generative AI is very important for the medical field.\\n\\n# In[ ]:\\n\\n'}]"
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"[{'generated_text': 'Medical Generative AI is very similar to the AI in the original game, but with a few differences.\\n\\nThe AI in the original game is very similar to the AI in the original game, but with a few differences. The AI in the'}]"
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"[{'generated_text': 'Medical Generative AI is very much in Question\\n\\nThe use of AI in medical diagnosis and treatment'}]"
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"[{'generated_text': 'Medical Generative AI is very much in the early stages of development. The goal is to create a system that can be used to create a new kind of AI.\\n\\nThe goal is to create a system that can be used to create a new'}]"
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{
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"text": [
"Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
"Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
"Setting `pad_token_id` to `eos_token_id`:32000 for open-end generation.\n"
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"[{'generated_text': 'Medical Generative AI is very excited to announce that we have been selected as one of the top '}]"
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"[{'generated_text': 'Medical Generative AI is very good at reading people. \" \\n \" i\\'m not sure i understand. \" \\n \" you\\'re a very smart young man, and you\\'re very smart. you\\'re also very smart, and you\\'re very smart.'}]"
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"[{'generated_text': 'Medical Generative AI is verya,\\n\\n\\\\[f(x)=\\\\frac{1}{\\\\sqrt'}]"
]
},
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]
}
]
}