Update raw Item 1A text dataset

README.md

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+---
+license: other
+language:
+- en
+task_categories:
+- text-generation
+- feature-extraction
+pretty_name: Raw SEC Item 1A Risk Factors Text
+size_categories:
+- "1K<n<10K"
+tags:
+- finance
+- sec-filings
+- 10-k
+- risk-factors
+- item-1a
+- edgar
+- raw-text
+- document-corpus
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/item1a_raw.parquet
+---
+
+# Raw SEC Item 1A Risk Factors Text
+
+## Dataset Summary
+
+This dataset is an English-language corpus of Item 1A Risk Factors sections
+from SEC Form 10-K annual filings. Each row represents one curated
+company-year filing. The corpus was created for research on temporal changes
+in corporate risk disclosure, with particular interest in environmental risk
+language.
+
+The dataset contains raw disclosure text only. It does not include
+environmental enforcement records, annotations, model outputs, labels or
+scores.
+
+## Supported Tasks
+
+This is a document corpus, not a supervised benchmark. It can support document
+representation learning, retrieval over risk-factor disclosures, topic
+modeling, lexical analysis and temporal analysis of corporate risk language.
+
+## Languages
+
+The text is English (`en`) and comes from U.S. SEC filings.
+
+## What Item 1A Means
+
+SEC Form 10-K is the annual report that many U.S. public companies file with
+the U.S. Securities and Exchange Commission. Item 1A is the Risk Factors
+section of that filing. In this section, companies describe material
+uncertainties that could affect their business, financial condition or future
+results.
+
+A risk factor is company-written disclosure about something that could go
+wrong. It can cover regulation, litigation, commodity prices, climate exposure,
+operations, supply chains, financing, cybersecurity, competition or broader
+macroeconomic conditions. The text is useful for studying how firms describe
+risk over time, but it is not evidence that a risk has already happened.
+
+## Dataset Structure
+
+### Data Instances
+
+Each example contains a document identifier and the extracted Item 1A text.
+
+```json
+{
+  "file_name": "AEE_2019.txt",
+  "text": "<full Item 1A Risk Factors text>"
+}
+```
+
+### Data Fields
+
+The default config contains a two-column Parquet corpus:
+
+| Field | Type | Description |
+|---|---|---|
+| `file_name` | string | Source raw file name in `TICKER_YEAR.txt` format. |
+| `text` | string | Full raw Item 1A Risk Factors text. |
+
+### Data Splits
+
+| Split | Rows |
+|---|---:|
+| train | 1,088 |
+
+The dataset has one split because it is a raw document corpus rather than a
+train, validation and test benchmark.
+
+## Dataset Statistics
+
+| Field | Value |
+|---|---:|
+| Raw text rows | 1,088 |
+| Companies identified from file names | 138 |
+| Fiscal years identified from file names | 2015-2024 |
+| Total raw words | 10,482,592 |
+| Mean raw words per row | 9,635 |
+| Total raw characters | 71,329,285 |
+
+## Dataset Creation
+
+### Source Data
+
+| Source | Role in this dataset |
+|---|---|
+| SEC EDGAR Form 10-K filings | Original public annual reports filed by U.S. public companies. |
+| Item 1A Risk Factors | The extracted section that contains company-written descriptions of material business risks. |
+| `edgartools` | Retrieval and extraction helper used to access 10-K filings and resolve their Item 1A sections. |
+
+The original language producers are public companies filing annual reports
+with the U.S. Securities and Exchange Commission. The source records are
+public Form 10-K filings available through SEC EDGAR.
+
+### Curation Rationale
+
+The corpus was built to support analysis of how corporate risk-factor language
+changes over time. Keeping the release at the raw Item 1A document level makes
+the source text usable for different NLP tasks without imposing predefined
+labels or scores.
+
+### Collection And Processing
+
+Form 10-K annual filings were retrieved from SEC EDGAR. Item 1A sections were
+resolved with `edgartools`, then retained as document-level observations when
+the extracted risk-factor section was usable. The released text is not
+whitespace-normalized, sentence-split, keyword filtered, labeled or scored.
+Document identifiers preserve the `TICKER_YEAR.txt` naming convention used
+during extraction.
+
+### Annotations
+
+The dataset contains no human or machine annotations.
+
+## Loading
+
+```python
+from datasets import load_dataset
+
+docs = load_dataset("MichaelDG/esg-commitment-verifiability", split="train")
+first_doc = docs[0]
+print(first_doc["file_name"])
+print(first_doc["text"][:500])
+```
+
+## Intended Use And Limitations
+
+Use this dataset for NLP research on SEC risk-factor language. It can support
+document representation learning, retrieval, topic modeling and temporal text
+analysis.
+
+The corpus is not representative of all public companies or all SEC filings.
+It covers curated Item 1A documents from identified company-years in the
+2015-2024 fiscal-year window. Item 1A is broader than ESG disclosure.
+Environmental language may be absent from some documents.
+
+Risk-factor text is company-written disclosure about possible risks. It should
+not be treated as evidence that a risk occurred, legal advice, investment
+advice or an environmental performance score.
+
+## Personal And Sensitive Information
+
+The dataset consists of public company filings. It is not designed to identify
+private individuals, although source filings may contain names or legal matter
+details when companies include them in public disclosures.
+
+## Licensing Information
+
+The source texts come from public SEC filings. Users are responsible for
+complying with SEC EDGAR access terms and any rights that may apply to the
+source filing text.
+
+## Citation Information
+
+Campbell, J. L., Chen, H., Dhaliwal, D. S., Lu, H. and Steele, L. B. (2014).
+The information content of mandatory risk factor disclosures in corporate
+filings. *Review of Accounting Studies*, 19(1), 396-455.
+[https://doi.org/10.1007/s11142-013-9258-3](https://doi.org/10.1007/s11142-013-9258-3)
+
+Loughran, T. and McDonald, B. (2011). When is a liability not a liability?
+Textual analysis, dictionaries and 10-Ks. *Journal of Finance*, 66(1), 35-65.
+[https://doi.org/10.1111/j.1540-6261.2010.01625.x](https://doi.org/10.1111/j.1540-6261.2010.01625.x)

data/item1a_raw.parquet

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