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"source": [
"# FinBert\n",
"\n",
"## Sources\n",
"\n",
"[HugginFace](https://huggingface.co/ProsusAI/finbert)\n",
"[Tutorial](https://medium.com/codex/stocks-news-sentiment-analysis-with-deep-learning-transformers-and-machine-learning-cdcdb827fc06)"
]
},
{
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"metadata": {
"ExecuteTime": {
"end_time": "2023-04-30T21:54:44.056694Z",
"start_time": "2023-04-30T21:53:45.027971Z"
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"text": [
"ERROR: To modify pip, please run the following command:\n",
"C:\\Users\\phhor\\PycharmProjects\\aki_prj23_transparenzregister\\venv\\Scripts\\python.exe -m pip install transformers tqdm pandas numpy torch torchvision torchaudio pip -Uq\n",
"\n",
"[notice] A new release of pip is available: 23.0.1 -> 23.1.2\n",
"[notice] To update, run: python.exe -m pip install --upgrade pip\n"
]
}
],
"source": [
"!pip install transformers tqdm pandas numpy torch torchvision torchaudio pip -Uq"
]
},
{
"cell_type": "code",
"execution_count": 16,
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"source": [
"import pandas as pd\n",
"from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
"\n",
"# create a tokenizer object\n",
"tokenizer = AutoTokenizer.from_pretrained(\"ProsusAI/finbert\")\n",
"\n",
"# fetch the pretrained model\n",
"model = AutoModelForSequenceClassification.from_pretrained(\"ProsusAI/finbert\")"
]
},
{
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"execution_count": 12,
"metadata": {},
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],
"source": [
"# A headline to be used as input\n",
"import torch\n",
"\n",
"headline = \"Microsoft fails to hit profit expectations\"\n",
"headline2 = (\n",
" \"Am Aktienmarkt überwieg weiter die Zuversicht, wie der Kursverlauf des DAX zeigt.\"\n",
")\n",
"\n",
"# Pre-process input phrase\n",
"input_tokens = tokenizer(headline2, padding=True, truncation=True, return_tensors=\"pt\")\n",
"# Run inference on the tokenized phrase\n",
"output = model(**input_tokens)\n",
"\n",
"# Pass model output logits through a softmax layer.\n",
"sentim_scores = torch.nn.functional.softmax(output.logits, dim=-1)\n",
"sentim_scores"
]
},
{
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"def analyze_sentiment(text: str):\n",
" print(text)\n",
" input_tokens = tokenizer(text, padding=True, truncation=True, return_tensors=\"pt\")\n",
" output = model(**input_tokens)\n",
" return pd.Series(\n",
" torch.nn.functional.softmax(output.logits, dim=-1)[0].data,\n",
" index=[\"+\", \"0\", \"-\"],\n",
" )\n",
"\n",
"\n",
"tf = analyze_sentiment(headline)\n",
"tf"
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"Microsoft fails to hit profit expectations\n",
"Am Aktienmarkt überwieg weiter die Zuversicht, wie der Kursverlauf des DAX zeigt.\n",
"Stocks rallied and the British pound gained.\n",
"Meyer Burger bedient ab sofort australischen Markt und präsentiert sich auf Smart Energy Expo in Sydney.\n",
"Meyer Burger enters Australian market and exhibits at Smart Energy Expo in Sydney.\n",
"J&T Express Vietnam hilft lokalen Handwerksdörfern, ihre Reichweite zu vergrößern.\n",
"7 Experten empfehlen die Aktie zum Kauf, 1 Experte empfiehlt, die Aktie zu halten.\n",
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"Microsoft aktie steigt.\n"
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"def analyse_sentiments(texts: pd.Series) -> pd.DataFrame:\n",
" values = texts[\"text\"].apply(analyze_sentiment)\n",
" # print(values)\n",
" texts[[\"+\", \"0\", \"-\"]] = values\n",
" return texts\n",
"\n",
"\n",
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