speedplane's picture
Update README.md
97079d1 verified
metadata
license: apache-2.0
task_categories:
  - text-classification
  - zero-shot-classification
  - feature-extraction
language:
  - en
tags:
  - legal
  - finance
size_categories:
  - 10K<n<100K
pretty_name: Time Entries and Phases
dataset_info:
  features:
    - name: EntryID
      dtype: int64
    - name: Narrative
      dtype: string
    - name: PhaseName
      dtype: string
    - name: PhaseDurationDays
      dtype: float64
    - name: EntryDurationHours
      dtype: float64
    - name: Timekeeper
      dtype: string
    - name: Rate
      dtype: float64
    - name: Position
      dtype: string
    - name: Partner
      dtype: int64
    - name: Firm
      dtype: string
    - name: Date
      dtype: string
    - name: Docket
      dtype: string
    - name: SourceDocumentZip
      dtype: string
    - name: SourceDocumentName
      dtype: string
    - name: QuerySlug
      dtype: string
    - name: Length
      dtype: float64
    - name: Pages
      dtype: int64
    - name: Court
      dtype: string
    - name: DocketEntry
      dtype: string
    - name: Link
      dtype: string
    - name: LinkEx
      dtype: string
    - name: TextMatter
      dtype: string
    - name: DaysFromStart
      dtype: float64
    - name: Matter
      dtype: string
    - name: Cleaned
      dtype: int64
    - name: Confidence
      dtype: string
    - name: Analysis
      dtype: string
    - name: __index_level_0__
      dtype: int64
  splits:
    - name: train
      num_bytes: 9834936
      num_examples: 12417
  download_size: 694278
  dataset_size: 9834936
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*

Litigation Billable Hours: Time-Entries and Phases Dataset

AI-ready, real-world time-entries mapped to litigation phases.

A datset of hours billed to 31 different litigation matters, with the names of the timekeeper, narrative description, duration, date, and other meta-information.

Why release this data?

  • Establish an open-source baseline. Time entries are typically confidential. Open data accelerates research.
  • Public provenance. Every row comes from an exhibit to a Motion for Attorneys’ Fees, these are public time entries.
  • Encourage reproducible benchmarks. A common dataset lets the community compare narrative classifiers and cost models.
  • Teach. Useful for classroom demos on legal operations data.
  • Down-stream Products Seed data for proof-of-concepts.

Brought to you by

Created by LexPipe, which sourced time entries from public records. LexPipe found examples of full attorney invoices as exhibits to Motion for Attoneys Fees. The result is the first set of public royalty free time entries for research and commerce.

Whether you’re a law firm, ALSP, or researcher, email hello@lexpipe.com to:

  • advise on bespoke benchmarks
  • provide clean public data, or
  • integrate your private data securely.

No cost consultation: https://www.lexpipe.com/meetings/lexpipe/meet-us


Time Entry Dataset at a Glance

  • 31 litigation matters
  • ≈13k unique time entries
  • ≈20k hours of billed time
  • 4 phases labeled: Pleading, Discovery, Pretrial, Trial

Law firm invoices were OCRed with tersseract 3.0, LLMs extracted time entries, with manual data cleaning. Source PDF documents available on request.

Sample: New York Commercial Contract Case

Time entries for an example matter are shown for a New York commercial contract dispute:

Title Cowen and Company, LLC v. ReShape Lifesciences, Inc.
Docket 654817/2021
Court New York State, New York County, Supreme Court
Link NYSCEF Link to Docket
Summary Cowen and Company, LLC alleges that ReShape Lifesciences, Inc., breached a 2019 financial advisory agreement by failing to pay $1.35 million in fees after a merger.
Phases The case advanced through pleading from August 2021, discovery until June 2022, and pretrial motions leading to a money judgment on May 11, 2023, after which it was disposed.

Time entries for the case can be seen below, with the time coded according to the phase.

image/png

Sample Rows


Detailed Usage

This repository contains both raw and cleaned data.

from datasets import load_dataset
repo_id = "lexpipe/time-entries-and-phases"
dataset = load_dataset(repo_id)

Each time entry has an associated duration, and (when available) a name for the phase of legal proceedings. The associated duration is the amount of time logged by the person filing the time entry.

Narratives

Each time entry has a "narrative", a textual descriptions of the work that was conducted by the timekeeper making the entry. Narratives vary in length and detail. In the cleaned data, the narratives have been processed with named entity recognition, with names of people and organizations scrubbed and replaced with "PERSON". The real names of the timekeepers are kept in the respective column.

Certain phrases occur often in the narratives, and can be indicative of the phase of the legal matter. Word size indicates frequency of occurrence in the narratives:

Data Columns

For a description of the columns in the processed data, please see the exploratory data analysis notebook

The (raw) dataset comprises the following columns:

Column Column Name Description Data Type
1 EntryID Unique identifier of the entry. Integer
2 Narrative Description of the work performed during the time entry. Object (string)
3 PhaseName Name of the phase in the legal proceedings. Object (string)
4 PhaseDurationDays Duration of the legal phase in days. Float64
5 EntryDurationHours Total duration of the legal services for the entry (in hours). Float64
6 Timekeeper Name of the person logging the time entry. Object (string)
7 Rate Hourly rate of the timekeeper. Float64
8 Position Position or role of the timekeeper (e.g., associate, partner). Object (string)
9 Partner Is the time keeper a partner in the firm or not Bool
10 Firm Law firm name associated with the time entry. Object (string)
11 Date Date of the time entry. Object (string)
12 Docket Unique identifier for the case in the docket system. Object (string)
13 SourceDocumentZip Name of the .zip file containing the source document. Object (string)
14 SourceDocumentName Name of the source document (PDF). Object (string)
15 QuerySlug Unique identifier or slug for the data entry. Object (string)
16 Length Length of the document in tokens. Integer
17 Pages Pages containing the time entry data. Object (string)
18 Court Court presiding over the matter. Object (string)
19 DocketEntry Text of the specific docket entry. Object (string)
20 Link Link to the docket entry or relevant document. Object (string)
21 LinkEx External link related to the time entry. Object (string)
22 TextMatter Full or cleaned text related to the matter. Object (string)
23 DaysFromStart Days from the start of the case to the time entry. Float64
24 Matter Identifier or name of the legal matter. Object (string)
25 Cleaned Has the entry been cleaned or not. Bool
26 Confidence Confidence score for the extracted data. Object (string)
27 Analysis Additional analysis or notes on the time entry. Object (string)

Statistics of data columns

Column Name Mean Median Range (max minus min) Cardinality
PhaseDurationDays 365.15 367.00 2023.00 31
EntryDurationHours 1.49 0.70 17.60 172
Rate 578.78 590.00 1507.00 180
Length 268666.61 201498.00 571675.00 46
DaysFromStart 449.63 435.00 1942.00 613

Data Splits

No predefined splits for machine learning purposes have been made, users may create splits (e.g., train/test/validation) as needed.

If treated cross-sectionally, predictive perforamnce of time entry narrative to phase name label drops progressively. From roughly ~2000 narratives predictive performance is adequate.

image/png

Contributions

  • Michael Sander, CEO / Founder, LexPipe, Inc.

  • Ruud van den Brink, Principal Data Scientist, LexPipe, Inc.

Additional Information

Released under Apache-2.0, which gives broad commercial & research rights. Please attribute “LexPipe” and link back to this repo.

https://lexpipe.com