Datasets:

danish-foundation-models
/

danish-dynaword

@@ -226,13 +226,7 @@ https://github.com/huggingface/datasets/blob/main/templates/README_guide.md
 The Danish dynaword is a collection of Danish free-form text datasets from various domains. All of the datasets in Danish Dynaword are openly licensed
 and deemed permissible for training large language models.
-Danish Dynaword is continually developed, which means that the dataset will actively be updated as new datasets become available. If you would like to contribute a dataset see the [contribute section](#contributing-to-the-dataset)
-<p align="center">
-<img src="./images/tokens_over_time.svg" width="400" style="margin-right: 10px;" />
-</p>
 ### Loading the dataset
@@ -417,11 +411,18 @@ You can learn more about each dataset by pressing the link in the first column.
 ### Data Collection and Processing
 The data collection and processing varies depending on the dataset and is documentationed the individual datasheets, which is linked in the above table. If possible the collection is documented both in the datasheet and in the reproducible script (`data/{dataset}/create.py`).
 In addition to data specific processing we also run a series automated quality checks to ensure formatting (e.g. ensuring correctly formatted columns and unique IDs), quality checks (e.g. duplicate and empty string detection) and datasheet documentation checks. These checks are there to ensure a high quality of documentation and a minimal level of quality. To allow for the development of novel cleaning methodologies we do not provide more extensive cleaning.
 ### Dataset Statistics
 The following plot show the domains distribution of the following within the dynaword:
@@ -441,6 +442,7 @@ The following plot show the domains distribution of the following within the dyn
 ### Contributing to the dataset
 We welcome contributions to the dataset such as new sources, better data filtering and so on. To get started on contributing please see [the contribution guidelines](CONTRIBUTING.md)

 The Danish dynaword is a collection of Danish free-form text datasets from various domains. All of the datasets in Danish Dynaword are openly licensed
 and deemed permissible for training large language models.
+Danish Dynaword is continually developed, which means that the dataset will actively be updated as new datasets become available. If you would like to contribute a dataset see the [contribute section](#contributing-to-the-dataset).
 ### Loading the dataset
 ### Data Collection and Processing
+Danish Dynaword is continually developed, which means that the dataset will actively be updated as new datasets become available. This means that the size of Dynaword increases over time as seen in the following plot:
+<p align="center">
+<img src="./images/tokens_over_time.svg" width="600" style="margin-right: 10px;" />
+</p>
 The data collection and processing varies depending on the dataset and is documentationed the individual datasheets, which is linked in the above table. If possible the collection is documented both in the datasheet and in the reproducible script (`data/{dataset}/create.py`).
 In addition to data specific processing we also run a series automated quality checks to ensure formatting (e.g. ensuring correctly formatted columns and unique IDs), quality checks (e.g. duplicate and empty string detection) and datasheet documentation checks. These checks are there to ensure a high quality of documentation and a minimal level of quality. To allow for the development of novel cleaning methodologies we do not provide more extensive cleaning.
 ### Dataset Statistics
 The following plot show the domains distribution of the following within the dynaword:
 ### Contributing to the dataset
 We welcome contributions to the dataset such as new sources, better data filtering and so on. To get started on contributing please see [the contribution guidelines](CONTRIBUTING.md)

data/health_hovedstaden/health_hovedstaden.md CHANGED Viewed

@@ -26,7 +26,7 @@ The document collection consists of guidelines and informational documents for h
 The corpus was created based on the texts in the document collection and has been post-processed so that the texts can be used for the development of language technology.
-Martin Sundahl Laursen and Thiusius R. Savarimuthu from the University of Southern Denmark have assisted the Danish Agency for Digital Government with the post-processing of the data. Read their joint paper on "Automatic Annotation of Training Data for Deep Learning Based De-identification of Narrative Clinical Text."


26
27	The corpus was created based on the texts in the document collection and has been post-processed so that the texts can be used for the development of language technology.
28
29	+ Martin Sundahl Laursen and Thiusius R. Savarimuthu from the University of Southern Denmark have assisted the Danish Agency for Digital Government with the post-processing of the data. Read their joint paper on "[Automatic Annotation of Training Data for Deep Learning Based De-identification of Narrative Clinical Text](https://ceur-ws.org/Vol-3416/paper_5.pdf)."
30
31
32

images/tokens_over_time.html ADDED Viewed

	@@ -0,0 +1,7 @@

+<html>
+<head><meta charset="utf-8" /></head>
+<body>
+    <div>                        <script type="text/javascript">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>
+        <script charset="utf-8" src="https://cdn.plot.ly/plotly-3.0.1.min.js"></script>                <div id="a8776fd6-9bc6-41ad-94a0-22c4d1eaf95e" class="plotly-graph-div" style="height:400px; width:600px;"></div>            <script type="text/javascript">                window.PLOTLYENV=window.PLOTLYENV || {};                                if (document.getElementById("a8776fd6-9bc6-41ad-94a0-22c4d1eaf95e")) {                    Plotly.newPlot(                        "a8776fd6-9bc6-41ad-94a0-22c4d1eaf95e",                        [{"hovertemplate":"%{text}\u003cextra\u003e\u003c\u002fextra\u003e","line":{"color":"#DC2626","width":3},"marker":{"color":"#DC2626","size":5},"mode":"lines+markers","name":"Tokens","text":["Date: 2025-01-02\u003cbr\u003eTokens: 1.57G\u003cbr\u003eSamples: 546,769\u003cbr\u003eCommit: 9c15515d\u003cbr\u003eMessage: Added number of llama3 tokens to desc stats","Date: 2025-01-03\u003cbr\u003eTokens: 1.84G\u003cbr\u003eChange: +271.89M\u003cbr\u003eSamples: 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images/tokens_over_time.png ADDED Viewed

Git LFS Details

SHA256: 9a386669e234a230401851b9ac9ec96c1a1958a84e183d3e5e66cf8a7bfec53b
Pointer size: 130 Bytes
Size of remote file: 97.7 kB

images/tokens_over_time.svg ADDED Viewed

src/dynaword/plot_tokens_over_time.py ADDED Viewed

	@@ -0,0 +1,241 @@

+import json
+import logging
+import subprocess
+from datetime import datetime
+from typing import Any, Dict, List, Optional, Tuple
+import pandas as pd
+import plotly.graph_objects as go
+from dynaword.paths import repo_path
+# Configure logging
+logging.basicConfig(
+    level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s"
+)
+logger = logging.getLogger(__name__)
+def get_file_history(
+    filename: str = "descriptive_stats.json",
+) -> List[Tuple[str, str, str]]:
+    """Get commit history for a file with commit messages"""
+    logger.info(f"Retrieving git history for {filename}")
+    cmd = [
+        "git",
+        "log",
+        "--format=%H|%ci|%s",  # commit hash | commit date | subject
+        "--",
+        filename,
+    ]
+    try:
+        result = subprocess.run(
+            cmd, capture_output=True, text=True, cwd=repo_path, check=True
+        )
+        commits = []
+        for line in result.stdout.strip().split("\n"):
+            if line:
+                parts = line.split("|", 2)  # Split on first 2 pipes only
+                if len(parts) == 3:
+                    commit_hash, date_str, message = parts
+                    commits.append((commit_hash, date_str, message))
+        logger.info(f"Found {len(commits)} commits for {filename}")
+        return commits
+    except subprocess.CalledProcessError as e:
+        logger.error(f"Failed to get git history: {e}")
+        return []
+def get_file_at_commit(commit_hash: str, filename: str) -> Optional[Dict[str, Any]]:
+    """Get file content at specific commit"""
+    cmd = ["git", "show", f"{commit_hash}:{filename}"]
+    try:
+        result = subprocess.run(
+            cmd, capture_output=True, text=True, cwd=repo_path, check=True
+        )
+        return json.loads(result.stdout)
+    except (subprocess.CalledProcessError, json.JSONDecodeError) as e:
+        logger.warning(f"Failed to parse {filename} at commit {commit_hash[:8]}: {e}")
+        return None
+def create_token_dataframe(filename: str = "descriptive_stats.json") -> pd.DataFrame:
+    """Create DataFrame with token history from git commits"""
+    logger.info("Building token history dataframe from git commits")
+    commits = get_file_history(filename)
+    if not commits:
+        logger.warning("No commits found")
+        return pd.DataFrame()
+    data = []
+    for commit_hash, date_str, commit_message in commits:
+        file_data = get_file_at_commit(commit_hash, filename)
+        if file_data and "number_of_tokens" in file_data:
+            try:
+                date = datetime.fromisoformat(date_str.split(" ")[0])
+                data.append(
+                    {
+                        "date": date,
+                        "tokens": file_data["number_of_tokens"],
+                        "samples": file_data.get("number_of_samples", 0),
+                        "avg_length": file_data.get("average_document_length", 0),
+                        "commit": commit_hash,
+                        "commit_short": commit_hash[:8],
+                        "commit_message": commit_message,
+                    }
+                )
+            except ValueError as e:
+                logger.warning(f"Failed to parse date {date_str}: {e}")
+    # Convert to DataFrame and sort by date
+    df = pd.DataFrame(data)
+    if df.empty:
+        logger.warning("No valid data found in commits")
+        return df
+    df = df.sort_values("date").reset_index(drop=True)
+    # Calculate token changes
+    if len(df) > 1:
+        df["token_change"] = df["tokens"].diff()
+    logger.info(
+        f"Created dataframe with {len(df)} data points spanning {df['date'].min().date()} to {df['date'].max().date()}"
+    )
+    return df
+def _format_tokens(value: float) -> str:
+    """Format tokens with human-readable suffixes"""
+    if value >= 1e12:
+        return f"{value/1e12:.2f}T"
+    elif value >= 1e9:
+        return f"{value/1e9:.2f}G"
+    elif value >= 1e6:
+        return f"{value/1e6:.2f}M"
+    elif value >= 1e3:
+        return f"{value/1e3:.2f}k"
+    else:
+        return f"{value:.0f}"
+def _create_hover_text(df: pd.DataFrame) -> List[str]:
+    """Create hover text for each data point"""
+    hover_text = []
+    for _, row in df.iterrows():
+        hover_info = (
+            f"Date: {row['date'].strftime('%Y-%m-%d')}<br>"
+            f"Tokens: {_format_tokens(row['tokens'])}<br>"
+        )
+        if pd.notna(row.get("token_change")):
+            change_sign = "+" if row["token_change"] >= 0 else ""
+            hover_info += (
+                f"Change: {change_sign}{_format_tokens(abs(row['token_change']))}<br>"
+            )
+        hover_info += (
+            f"Samples: {row['samples']:,}<br>"
+            f"Commit: {row['commit_short']}<br>"
+            f"Message: {row['commit_message']}"
+        )
+        hover_text.append(hover_info)
+    return hover_text
+def _add_reference_lines(fig: go.Figure) -> None:
+    """Add reference lines for other Danish corpora"""
+    references = [
+        (300_000_000, "Common Corpus (dan) (Langlais et al., 2025)"),
+        (1_000_000_000, "Danish Gigaword (Derczynski et al., 2021)"),
+    ]
+    for y_value, annotation in references:
+        fig.add_hline(
+            y=y_value,
+            line_dash="dash",
+            line_color="gray",
+            line_width=1,
+            annotation_text=annotation,
+            annotation_position="top left",
+            annotation_font_size=12,
+            annotation_font_color="gray",
+        )
+def plot_tokens_over_time(
+    df: pd.DataFrame, width: int = 600, height: int = 400
+) -> go.Figure:
+    """Plot tokens over time using Plotly with interactive hover info"""
+    hover_text = _create_hover_text(df)
+    # Create the plot
+    fig = go.Figure()
+    # Add main data line
+    fig.add_trace(
+        go.Scatter(
+            x=df["date"],
+            y=df["tokens"],
+            mode="lines+markers",
+            name="Tokens",
+            line=dict(width=3, color="#DC2626"),  # Saturated red
+            marker=dict(size=5, color="#DC2626"),
+            hovertemplate="%{text}<extra></extra>",
+            text=hover_text,
+        )
+    )
+    # Add reference lines
+    _add_reference_lines(fig)
+    # Update layout
+    fig.update_layout(
+        title="Number of Tokens Over Time in Danish Dynaword",
+        xaxis_title="Date",
+        yaxis_title="Number of Tokens (Llama 3)",
+        hovermode="closest",
+        width=width,
+        height=height,
+        showlegend=False,
+        plot_bgcolor="rgba(0,0,0,0)",  # Transparent plot background
+        paper_bgcolor="rgba(0,0,0,0)",  # Transparent paper background
+    )
+    # Set x-axis and y-axis properties
+    # x_min = df["date"].min() - pd.Timedelta(days=)
+    # x_max = df["date"].max() + pd.Timedelta(days=1)
+    # Format y-axis
+    fig.update_yaxes(tickformat=".2s", ticksuffix="")
+    # fig.update_xaxes(range=[x_min, x_max])  # Explicitly set x-axis range
+    return fig
+def create_tokens_over_time_plot() -> None:
+    """Main function to create DataFrame and plot tokens over time"""
+    df = create_token_dataframe()
+    if not df.empty:
+        logger.info("Generating interactive plot")
+        fig = plot_tokens_over_time(df)
+    else:
+        logger.warning("No data available to plot")
+    save_path = repo_path / "images" / "tokens_over_time.html"
+    save_path_svg = repo_path / "images" / "tokens_over_time.svg"
+    save_path.parent.mkdir(parents=True, exist_ok=True)
+    fig.write_html(save_path, include_plotlyjs="cdn")
+    fig.write_image(save_path_svg)
+if __name__ == "__main__":
+    create_tokens_over_time_plot()

src/dynaword/update_descriptive_statistics.py CHANGED Viewed

@@ -23,6 +23,7 @@ from dynaword.git_utilities import (
     get_latest_revision,
 )
 from dynaword.paths import repo_path
 from dynaword.tables import create_overview_table, create_overview_table_str
 logger = logging.getLogger(__name__)
@@ -106,6 +107,7 @@ def update_dataset(
         package = create_overview_table_str()
         sheet.body = sheet.replace_tag(package=package, tag="MAIN TABLE")
         create_domain_distribution_plot()
     sheet.write_to_path()

     get_latest_revision,
 )
 from dynaword.paths import repo_path
+from dynaword.plot_tokens_over_time import create_tokens_over_time_plot
 from dynaword.tables import create_overview_table, create_overview_table_str
 logger = logging.getLogger(__name__)
         package = create_overview_table_str()
         sheet.body = sheet.replace_tag(package=package, tag="MAIN TABLE")
         create_domain_distribution_plot()
+        create_tokens_over_time_plot()
     sheet.write_to_path()