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--- |
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language: |
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- kk |
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- tr |
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- ru |
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- en |
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language_details: eng_Latn, kaz_Cyrl, rus_Cyrl, tur_Latn |
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metrics: |
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- bleu |
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- chrf |
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pipeline_tag: translation |
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inference: false |
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datasets: |
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- facebook/flores |
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- issai/kazparc |
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--- |
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# Tilmash |
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<p align = "justify"> |
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Tilmash was fine-tuned using Facebook’s <a href = "https://huggingface.co/facebook/nllb-200-distilled-1.3B">NLLB</a> model to enable machine translation for four languages—Kazakh, Russian, English, and Turkish. |
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Below are the <a href = "https://huggingface.co/spaces/evaluate-metric/bleu">BLEU</a> | <a href = "https://huggingface.co/spaces/evaluate-metric/chrf">chrF</a> results of evaluating Tilmash on the <a href = "https://huggingface.co/datasets/facebook/flores">FLoRes</a> and <a href = "https://huggingface.co/datasets/issai/kazparc">KazParC</a> test datasets. |
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</p> |
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<table align = "center"> |
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<thead align = "center"> |
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<tr> |
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<th>Pair</th> |
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<th>FLoRes</th> |
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<th>KazParC</th> |
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</tr> |
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</thead> |
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<tbody align = "center"> |
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<tr> |
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<td>EN↔KK</td> |
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<td>0.20 | 0.60</td> |
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<td>0.21 | 0.60</td> |
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</tr> |
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<tr> |
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<td>EN↔RU</td> |
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<td>0.28 | 0.60</td> |
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<td>0.38 | 0.68</td> |
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</tr> |
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<tr> |
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<td>EN↔TR</td> |
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<td>0.27 | 0.65</td> |
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<td>0.25 | 0.64</td> |
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</tr> |
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<tr> |
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<td>KK↔EN</td> |
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<td>0.32 | 0.63</td> |
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<td>0.32 | 0.62</td> |
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</tr> |
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<tr> |
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<td>KK↔RU</td> |
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<td>0.18 | 0.52</td> |
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<td>0.29 | 0.63</td> |
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</tr> |
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<tr> |
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<td>KK↔TR</td> |
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<td>0.14 | 0.54</td> |
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<td>0.16 | 0.55</td> |
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</tr> |
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<tr> |
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<td>RU↔EN</td> |
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<td>0.32 | 0.63</td> |
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<td>0.42 | 0.70</td> |
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</tr> |
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<tr> |
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<td>RU↔KK</td> |
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<td>0.13 | 0.54</td> |
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<td>0.22 | 0.62</td> |
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</tr> |
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<tr> |
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<td>RU↔TR</td> |
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<td>0.14 | 0.54</td> |
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<td>0.18 | 0.57</td> |
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</tr> |
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<tr> |
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<td>TR↔EN</td> |
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<td>0.36 | 0.66</td> |
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<td>0.38 | 0.66</td> |
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</tr> |
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<tr> |
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<td>TR↔KK</td> |
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<td>0.13 | 0.54</td> |
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<td>0.16 | 0.55</td> |
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</tr> |
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<tr> |
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<td>TR↔RU</td> |
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<td>0.19 | 0.53</td> |
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<td>0.24 | 0.57</td> |
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</tr> |
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</tbody> |
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</table> |
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## Model Sources |
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- **Repository:** <a href = "https://github.com/IS2AI/KazParC">https://github.com/IS2AI/KazParC</a> |
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- **Paper:** <a href = "there_will_be_a_link_soon">KazParC: Kazakh Parallel Corpus for Machine Translation</a> |
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- **Demo:** <a href = "https://issai.nu.edu.kz/tilmash/">Tilmash Demo</a> |
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## How to Get Started with the Model |
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<p align = "justify">You can use this model with the Transformers pipeline for translation.</p> |
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```python |
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from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, TranslationPipeline |
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model = AutoModelForSeq2SeqLM.from_pretrained('issai/tilmash') |
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tokenizer = AutoTokenizer.from_pretrained("issai/tilmash") |
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# for src_lang and tgt_lang choose from kaz_Cyrl (Kazakh), rus_Cyrl (Russian), eng_Latn (English), tur_Latn (Turkish) |
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tilmash = TranslationPipeline(model = model, tokenizer = tokenizer, src_lang = "kaz_Cyrl", tgt_lang = "eng_Latn", max_length = 1000) |
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print(tilmash("Қазақстан — Шығыс Еуропа мен Орталық Азияда орналасқан мемлекет.")) |
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# [{'translation_text': 'Kazakhstan is a country located in Eastern Europe and Central Asia.'}] |
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``` |
