peft machine translation iva virtual assistants natural language understanding nlu pl dataset:iva_mt_wslot license:mit model-index region:us
---
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