|
--- |
|
license: apache-2.0 |
|
language: |
|
- en |
|
- la |
|
base_model: |
|
- distilbert/distilbert-base-multilingual-cased |
|
library_name: transformers |
|
datasets: |
|
- sjhuskey/latin_author_dll_id |
|
metrics: |
|
- f1 |
|
- accuracy |
|
--- |
|
|
|
# DLL Catalog Author Reconciliation Model |
|
|
|
The purpose of this model is to automate the reconciliation of bibliographic metadata with records in the [DLL Catalog](https://catalog.digitallatin.org/). |
|
|
|
The DLL Catalog maintains [authority records for authors and work records for works](https://catalog.digitallatin.org/authority-records). Each work is linked to its author (if known), and each [individual item record](https://catalog.digitallatin.orgindividual-items-catalog) must be linked to the relevant authority records and work records. |
|
|
|
## The Problem |
|
|
|
Linking incoming metadata for individual items to their corresponding author and work records in the DLL Catalog is the problem: the metadata that we acquire from other sites comes in different forms, with different spellings of authors' names and the titles of their works. Reconciling one or two records does not take much time, but more often than not we acquire many thousands of records all at once, which creates a significant logjam in the process of publishing records in the catalog. |
|
|
|
## The Proposed Solution |
|
|
|
The authority and work records in the DLL Catalog contain multiple variant spellings of author names and work titles, and new variant spellings are added to the records as we encounter them. This means that we already have a labeled set of data that could be used to train a model to identify names and titles and match them with the unique identifiers of the DLL Catalog's authority and work records. |
|
|
|
Achieving accuracy and reliability in this process will make the second goal of reconciling titles with their corresponding work records easier, since the author's name can be used to narrow the field of potential matches to the works by that author, thus reducing the chances for false positives on works with the same or similar titles. For example, both Julius Caesar and Lucan wrote works called _Bellum Civile_, and several authors wrote works known generically as _Carmina_. |
|
|
|
## The Model |
|
|
|
After preliminary experiments with sequential neural network models using [bag-of-words](https://en.wikipedia.org/wiki/Bag-of-words_model), [term frequency-inverse document frequency](https://en.wikipedia.org/wiki/Tf%E2%80%93idf) (tf-idf), and custom word embedding encoding, I settled on using a pretrained BERT model developed by [Devlin et al. 2018](https://arxiv.org/abs/1810.04805v2). Specifically, I'm using [Hugging Face's DistilBert base multilingual (cased) model](https://huggingface.co/distilbert/distilbert-base-multilingual-cased), which is based on work by [Sanh et al. 2020](https://doi.org/10.48550/arXiv.1910.01108). |
|
|
|
## Emissions |
|
|
|
Here is the `codecarbon` output from training on Google Colab with an A100 runtime: |
|
|
|
```properties |
|
timestamp: 2024-12-23T17:37:16 |
|
project_name: codecarbon |
|
run_id: a2b8975b-512b-4158-b41f-2a00d1d6fb39 |
|
experiment_id: 5b0fa12a-3dd7-45bb-9766-cc326314d9f1 |
|
duration (seconds): 877.531339527 |
|
emissions (kilograms of carbon): 0.0260658391490936 |
|
emissions_rate (kg/sec): 2.970359914797282e-05 |
|
cpu_power (average in watts): 42.5 |
|
gpu_power (average in watts): 71.5115170414632 |
|
ram_power (average in watts): 31.30389261245728 |
|
cpu_energy (total watts): 0.0103517333061409 |
|
gpu_energy (total watts): 0.03961337474623 |
|
ram_energy (total watts): 0.007623585574942 |
|
energy_consumed (total watts): 0.057588693627313 |
|
os: Linux-6.1.85+-x86_64-with-glibc2.35 |
|
python_version: 3.10.12 |
|
codecarbon_version: 2.8.2 |
|
cpu_count: 12 |
|
cpu_model: Intel(R) Xeon(R) CPU @ 2.20GHz |
|
gpu_count: 1 |
|
gpu_model: 1 x NVIDIA A100-SXM4-40GB |
|
ram_total_size: 83.47704696655273 |
|
tracking_mode: machine |
|
on_cloud: N |
|
pue: 1.0 |
|
``` |
|
|
