File size: 1,394 Bytes
e9e2022 d185548 e9e2022 d185548 e9e2022 d185548 e9e2022 d185548 e9e2022 d185548 e9e2022 d185548 e9e2022 d185548 e9e2022 d185548 e9e2022 d185548 e9e2022 d185548 e9e2022 d185548 e9e2022 d185548 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 |
---
library_name: transformers
license: mit
datasets:
- kde4
---
## Model Summary
dataequity-opus-mt-en-es is a Transformer based language translator fine tuned using the kde dataset. The base model used is Helsinki-NLP/opus-mt-en-es
Our model hasn't been fine-tuned through reinforcement learning from human feedback. The intention behind crafting this open-source model is to provide the research community with a non-restricted small model to explore vital safety challenges, such as reducing toxicity, understanding societal biases, enhancing controllability, and more.
### eng-spa
* source group: English
* target group: Spanish
* model: transformer
* source language(s): en
* target language(s): es
* model: transformer
### Inference Code:
```python
from transformers import MarianMTModel, MarianTokenizer,
hub_repo_name = 'sandeepsundaram/dataequity-opus-mt-en-es'
tokenizer = MarianTokenizer.from_pretrained(hub_repo_name)
finetuned_model = MarianMTModel.from_pretrained(hub_repo_name)
questions = [
"How are the first days of each season chosen?",
"Why are laws requiring identification for voting scrutinized by the media?",
"Why aren't there many new operating systems being created?"
]
translated = finetuned_model.generate(**tokenizer(questions, return_tensors="pt", padding=True))
[tokenizer.decode(t, skip_special_tokens=True) for t in translated]
``` |