|
--- |
|
license: apache-2.0 |
|
base_model: Helsinki-NLP/opus-mt-en-ROMANCE |
|
tags: |
|
- generated_from_trainer |
|
model-index: |
|
- name: seq2seq-finetuned-slang-en |
|
results: [] |
|
--- |
|
|
|
# seq2seq-finetuned-slang-en |
|
|
|
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ROMANCE](https://huggingface.co./Helsinki-NLP/opus-mt-en-ROMANCE) on a dataset of 1772 messages in slang English, and manually translated into standard English. |
|
There are some typos, since this was a first approach. Thus, insults or acronyms are not taken into account. Also, you may confirm mistakes, such as translating 'ya' almost always as 'yes', while it can also be 'you'. |
|
These are nuances I am working on. |
|
I am also working on this task for other languages, if you like the project, please contact me. |
|
|
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.0286 |
|
|
|
## Model description |
|
|
|
More information needed |
|
|
|
## Intended uses & limitations |
|
|
|
This is a prototype. I am working on a better model. The goal would be to finetune a model able to translate slang English into standard English. |
|
I am also working on applying it to different languages. |
|
|
|
## Training and evaluation data |
|
|
|
The data partition is as follows: 85% train and val, and 15% for testing. |
|
|
|
## Training procedure |
|
|
|
### Training hyperparameters |
|
|
|
The following hyperparameters were used during training: |
|
- learning_rate: 5e-05 |
|
- train_batch_size: 4 |
|
- eval_batch_size: 8 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- num_epochs: 3 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | |
|
|:-------------:|:-----:|:----:|:---------------:| |
|
| 0.0844 | 1.33 | 500 | 0.0714 | |
|
| 0.0297 | 2.65 | 1000 | 0.0286 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.35.2 |
|
- Pytorch 2.1.0+cu121 |
|
- Tokenizers 0.15.1 |
|
|
