transformers tensorboard safetensors distilbert text-classification en la el dataset:sjhuskey/greek_latin_authors base_model:distilbert/distilbert-base-multilingual-cased base_model:finetune:distilbert/distilbert-base-multilingual-cased license:apache-2.0 autotrain_compatible endpoints_compatible region:us
---
base_model:
- distilbert/distilbert-base-multilingual-cased
datasets:
- sjhuskey/greek_latin_authors
language:
- en
- la
- el
library_name: transformers
license: apache-2.0
metrics:
- f1
- accuracy
pipeline_tag: text-classification
---

# Greek and Latin Author Classifier

This model distinguishes the names of authors who primarily wrote in Ancient Greek from the names of authors who wrote primarily in Latin. 

The specific purpose for this model is to assist in processing bibliographic metadata about editions of Latin texts.

Most critical editions of ancient Greek texts bear a Latin version of the title of the original work and the author's name. For example, Hesiod's _Theogony_ is _Hesiodi Theogonia_ instead of _Ἡσιόδου Θεογονία_. Consequently, these works are often cataloged under the subject "Latin" by catalogers doing their best with languages they do not understand.

Consequently, metadata records tagged with the subject "Latin" from collections such as the [HathiTrust Digital Library](https://www.hathitrust.org/) inevitably include Greek works. 

Since the Digital Latin Library is interested only in records of Latin works, we need a good way of winnowing out the Greek editions. This model does a good job of that.

## Emissions

Here is the `codecarbon` output from training on Google Colab with an A100 runtime:

```properties
timestamp: 2024-12-25T15:16:45
project_name: codecarbon
run_id: f658b237-20c1-45cf-a8ee-cdb5e8521351
experiment_id: 5b0fa12a-3dd7-45bb-9766-cc326314d9f1
duration (seconds): 591.3640720729998
emissions (kilograms of carbon): 0.0178715223377235
emissions_rate (kg/sec): 3.0220845637570942e-05
cpu_power (average in watts): 42.5
gpu_power (average in watts): 105.04535891243202
ram_power (average in watts): 31.30389261245728
cpu_energy (total watts): 0.0069742756409347
gpu_energy (total watts): 0.025850867347344
ram_energy (total watts): 0.0051361307572122
energy_consumed (total watts): 0.037961273745491
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
```
