| --- |
| library_name: peft |
| license: mit |
| datasets: |
| - iva_mt_wslot |
| metrics: |
| - bleu |
| model-index: |
| - name: iva_mt_wslot-m2m100_418M-en-pl-lora_adapter |
| results: |
| - task: |
| name: Machine Translation |
| type: text2text-generation |
| dataset: |
| name: iva_mt_wslot |
| type: iva_mt_wslot |
| config: en-pl |
| split: validation |
| args: en-pl |
| metrics: |
| - name: Bleu |
| type: bleu |
| value: 38.2365 |
| language: |
| - pl |
| tags: |
| - machine translation |
| - iva |
| - virtual assistants |
| - natural language understanding |
| - nlu |
| --- |
| |
| # (WIP!) iva_mt_wslot-m2m100_418M-en-pl-lora_adapter |
|
|
| Notice: **Although training results are good for some reason inference results are rather poor. I'm leaving this model here as a PoC that PERF LORA adaptation for M2M100 is possible.** |
|
|
| This model is a LORA adapted version of [facebook/m2m100_418M](https://huggingface.co/facebook/m2m100_418M) on the iva_mt_wslot dataset. |
| It achieves the following results on the test set (measured with sacrebleu): |
| - Bleu: 9.33 |
|
|
| ## Using |
|
|
| The model can be used as follows: |
|
|
| First, clone the repository and navigate to the project directory: |
|
|
| ```bash |
| git clone https://github.com/cartesinus/multiverb_iva_mt |
| cd multiverb_iva_mt |
| ``` |
|
|
| Then: |
|
|
| ```python |
| import csv |
| from iva_mt.iva_mt import IVAMT |
| import pandas as pd |
| |
| lang = "es" |
| translator = IVAMT(lang, peft_model_id="cartesinus/iva_mt_wslot-m2m100_418M-en-es-lora_adapter", device="cuda:0", batch_size=128) |
| trans = translator.translate("here your example")[0] |
| ``` |
|
|
| ## Training results |
|
|
| | Epoch | Training Loss | Validation Loss | Bleu | Gen Len | |
| |:-----:|:-------------:|:---------------:|:-------:|:-------:| |
| | 1 | 7.8621 | 7.6870 | 24.9063 | 19.3322 | |
| | 2 | 7.6340 | 7.5312 | 29.7956 | 19.7533 | |
| | 3 | 7.5582 | 7.4595 | 34.8184 | 20.1269 | |
| | 4 | 7.5047 | 7.4264 | 36.1874 | 20.5621 | |
| | 5 | 7.4888 | 7.4167 | 36.2287 | 20.4417 | |
| | 6 | 7.4560 | 7.4013 | 36.6355 | 20.2241 | |
| | 7 | 7.4477 | 7.3907 | 37.0554 | 20.0945 | |
| | 8 | 7.4422 | 7.3743 | 37.7549 | 20.1589 | |
| | 9 | 7.4311 | 7.3748 | 37.5705 | 19.9370 | |
| | 10 | 7.4294 | 7.3679 | 37.5343 | 20.2241 | |
| | 11 | 7.4114 | 7.3697 | 38.1872 | 20.3836 | |
| | 12 | 7.4224 | 7.3620 | 38.1759 | 20.1785 | |
| | 13 | 7.4334 | 7.3608 | 38.0895 | 20.2996 | |
| | 14 | 7.4133 | 7.3621 | 38.2365 | 20.2948 | |
| | 15 | 7.4158 | 7.3599 | 38.1056 | 20.2010 | |
|
|
|
|
| ## Framework versions |
|
|
| - PEFT 0.5.0 |
