| language: | |
| - en | |
| - me | |
| - multilingual | |
| license: afl-3.0 | |
| tags: | |
| - translation | |
| datasets: | |
| - Qilex/EN-ME | |
| metrics: | |
| - bleu | |
| model-index: | |
| - name: en-me | |
| results: | |
| - task: | |
| type: translation | |
| name: translation en-me | |
| dataset: | |
| name: Qilex/EN-ME | |
| type: translation | |
| metrics: | |
| - type: bleu | |
| value: 17.2 | |
| This is a BART-large model finetuned on roughly 58000 aligned sentence pairs in English and Middle English, collected from the works of Geoffrey Chaucer, John Wycliffe, and the Gawain Poet. | |
| <br> | |
| It includes special characters such as �. | |
| <br> | |
| This model reflects the spelling inconsistencies characteristic of Middle English. | |
| <br> | |
| Because the model is trained largely on poetry and some prose, it is best at translating those sorts of tasks. | |
| <br> | |
| Performance can be improved by sentence tokenizing input data and translating sentence-by-sentence. | |
| <br> | |
| Removing contractions (hadn't -> had not) also boosts performance. | |