mirror of
https://github.com/fhswf/aki_prj23_transparenzregister.git
synced 2025-05-13 12:58:45 +02:00
Added a first trail with FinBert.
This commit is contained in:
parent
983c06eead
commit
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DD53EAC36AFB61B4
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- repo: https://gitlab.com/smop/pre-commit-hooks
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- repo: https://gitlab.com/smop/pre-commit-hooks
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rev: v1.0.0
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rev: v1.0.0
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hooks:
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hooks: []
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- id: check-poetry
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# - id: check-poetry
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- repo: https://github.com/Lucas-C/pre-commit-hooks-java
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- repo: https://github.com/Lucas-C/pre-commit-hooks-java
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rev: 1.3.10
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rev: 1.3.10
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387
Jupyter/AI-models/Sentiment Analysis/FinBert.ipynb
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387
Jupyter/AI-models/Sentiment Analysis/FinBert.ipynb
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"cells": [
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# FinBert\n",
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"\n",
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"## Sources\n",
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"\n",
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"[HugginFace](https://huggingface.co/ProsusAI/finbert)\n",
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"[Tutorial](https://medium.com/codex/stocks-news-sentiment-analysis-with-deep-learning-transformers-and-machine-learning-cdcdb827fc06)"
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]
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2023-04-30T21:54:44.056694Z",
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"start_time": "2023-04-30T21:53:45.027971Z"
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"outputs": [
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"ERROR: To modify pip, please run the following command:\n",
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"C:\\Users\\phhor\\PycharmProjects\\aki_prj23_transparenzregister\\venv\\Scripts\\python.exe -m pip install transformers tqdm pandas numpy torch torchvision torchaudio pip -Uq\n",
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"\n",
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"[notice] A new release of pip is available: 23.0.1 -> 23.1.2\n",
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"[notice] To update, run: python.exe -m pip install --upgrade pip\n"
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]
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}
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],
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"source": [
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"!pip install transformers tqdm pandas numpy torch torchvision torchaudio pip -Uq"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"metadata": {
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"collapsed": false,
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"jupyter": {
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},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
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"\n",
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"# create a tokenizer object\n",
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"tokenizer = AutoTokenizer.from_pretrained(\"ProsusAI/finbert\")\n",
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"\n",
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"# fetch the pretrained model\n",
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"model = AutoModelForSequenceClassification.from_pretrained(\"ProsusAI/finbert\")"
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]
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{
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"data": {
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"text/plain": [
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"tensor([[0.0535, 0.0279, 0.9185]], grad_fn=<SoftmaxBackward0>)"
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]
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},
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"execution_count": 12,
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"output_type": "execute_result"
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}
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],
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"source": [
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"# A headline to be used as input\n",
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"import torch\n",
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"\n",
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"headline = \"Microsoft fails to hit profit expectations\"\n",
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"headline2 = (\n",
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" \"Am Aktienmarkt überwieg weiter die Zuversicht, wie der Kursverlauf des DAX zeigt.\"\n",
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")\n",
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"\n",
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"# Pre-process input phrase\n",
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"input_tokens = tokenizer(headline2, padding=True, truncation=True, return_tensors=\"pt\")\n",
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"# Run inference on the tokenized phrase\n",
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"output = model(**input_tokens)\n",
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"\n",
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"# Pass model output logits through a softmax layer.\n",
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"sentim_scores = torch.nn.functional.softmax(output.logits, dim=-1)\n",
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"sentim_scores"
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]
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"text": [
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"Microsoft fails to hit profit expectations\n"
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]
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"data": {
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"text/plain": [
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"+ 0.034084\n",
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"0 0.932933\n",
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"- 0.032982\n",
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"dtype: float32"
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]
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"execution_count": 56,
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}
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],
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"source": [
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"def analyze_sentiment(text: str):\n",
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" print(text)\n",
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" input_tokens = tokenizer(text, padding=True, truncation=True, return_tensors=\"pt\")\n",
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" output = model(**input_tokens)\n",
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" return pd.Series(\n",
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" torch.nn.functional.softmax(output.logits, dim=-1)[0].data,\n",
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" index=[\"+\", \"0\", \"-\"],\n",
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" )\n",
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"\n",
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"\n",
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"tf = analyze_sentiment(headline)\n",
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"tf"
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]
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},
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"cell_type": "code",
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"execution_count": 69,
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>text</th>\n",
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" <th>lan</th>\n",
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" </thead>\n",
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" <td>en</td>\n",
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" <tr>\n",
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" <td>Am Aktienmarkt überwieg weiter die Zuversicht,...</td>\n",
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" <td>de</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>Stocks rallied and the British pound gained.</td>\n",
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" <td>en</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>Meyer Burger bedient ab sofort australischen M...</td>\n",
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" <td>de</td>\n",
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" text lan\n",
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"0 Microsoft fails to hit profit expectations en\n",
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"1 Am Aktienmarkt überwieg weiter die Zuversicht,... de\n",
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"2 Stocks rallied and the British pound gained. en\n",
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"3 Meyer Burger bedient ab sofort australischen M... de"
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"source": [
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"text_df = pd.DataFrame(\n",
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" [\n",
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" {\"text\": \"Microsoft fails to hit profit expectations\", \"lan\": \"en\"},\n",
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" {\n",
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" \"text\": \"Am Aktienmarkt überwieg weiter die Zuversicht, wie der Kursverlauf des DAX zeigt.\",\n",
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" \"lan\": \"de\",\n",
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" },\n",
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" {\"text\": \"Stocks rallied and the British pound gained.\", \"lan\": \"en\"},\n",
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" {\n",
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" \"text\": \"Meyer Burger bedient ab sofort australischen Markt und präsentiert sich auf Smart Energy Expo in Sydney.\",\n",
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" \"lan\": \"de\",\n",
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" },\n",
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" ]\n",
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")\n",
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"text_df"
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"Am Aktienmarkt überwieg weiter die Zuversicht, wie der Kursverlauf des DAX zeigt.\n",
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"Stocks rallied and the British pound gained.\n",
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"Meyer Burger bedient ab sofort australischen Markt und präsentiert sich auf Smart Energy Expo in Sydney.\n"
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|
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||||||
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|
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" <th>2</th>\n",
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" <td>en</td>\n",
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" <td>de</td>\n",
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"3 Meyer Burger bedient ab sofort australischen M... de 0.116597 0.012790 \n",
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"\n",
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" - \n",
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"0 0.032982 \n",
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"1 0.918522 \n",
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"2 0.067165 \n",
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]
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"execution_count": 71,
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}
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],
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"source": [
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"def analyse_sentiments(texts: pd.Series) -> pd.DataFrame:\n",
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" values = texts[\"text\"].apply(analyze_sentiment)\n",
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" # print(values)\n",
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" texts[[\"+\", \"0\", \"-\"]] = values\n",
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" return texts\n",
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"\n",
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"\n",
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"analyse_sentiments(text_df)"
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|
||||||
|
"metadata": {
|
||||||
|
"kernelspec": {
|
||||||
|
"display_name": "Python 3 (ipykernel)",
|
||||||
|
"language": "python",
|
||||||
|
"name": "python3"
|
||||||
|
},
|
||||||
|
"language_info": {
|
||||||
|
"codemirror_mode": {
|
||||||
|
"name": "ipython",
|
||||||
|
"version": 3
|
||||||
|
},
|
||||||
|
"file_extension": ".py",
|
||||||
|
"mimetype": "text/x-python",
|
||||||
|
"name": "python",
|
||||||
|
"nbconvert_exporter": "python",
|
||||||
|
"pygments_lexer": "ipython3",
|
||||||
|
"version": "3.11.0"
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"nbformat": 4,
|
||||||
|
"nbformat_minor": 4
|
||||||
|
}
|
||||||
5
pyproject.toml
Normal file
5
pyproject.toml
Normal file
@ -0,0 +1,5 @@
|
|||||||
|
[tool.isort]
|
||||||
|
profile = "black"
|
||||||
|
|
||||||
|
[tool.pylint.format]
|
||||||
|
max-line-length = "88"
|
||||||
Loading…
x
Reference in New Issue
Block a user