|
--- |
|
library_name: transformers |
|
language: |
|
- multilingual |
|
- bn |
|
- cs |
|
- de |
|
- en |
|
- et |
|
- fi |
|
- fr |
|
- gu |
|
- ha |
|
- hi |
|
- is |
|
- ja |
|
- kk |
|
- km |
|
- lt |
|
- lv |
|
- pl |
|
- ps |
|
- ru |
|
- ta |
|
- tr |
|
- uk |
|
- xh |
|
- zh |
|
- zu |
|
license: apache-2.0 |
|
base_model: answerdotai/ModernBERT-large |
|
tags: |
|
- quality-estimation |
|
- regression |
|
- generated_from_trainer |
|
datasets: |
|
- ymoslem/wmt-da-human-evaluation |
|
model-index: |
|
- name: Quality Estimation for Machine Translation |
|
results: |
|
- task: |
|
type: regression |
|
dataset: |
|
name: ymoslem/wmt-da-human-evaluation |
|
type: QE |
|
metrics: |
|
- name: Pearson Correlation |
|
type: Pearson |
|
value: 0.4458 |
|
- name: Mean Absolute Error |
|
type: MAE |
|
value: 0.1876 |
|
- name: Root Mean Squared Error |
|
type: RMSE |
|
value: 0.2393 |
|
- name: R-Squared |
|
type: R2 |
|
value: 0.1987 |
|
metrics: |
|
- pearsonr |
|
- mae |
|
- r_squared |
|
--- |
|
|
|
|
|
# Quality Estimation for Machine Translation |
|
|
|
This model is a fine-tuned version of [answerdotai/ModernBERT-large](https://huggingface.co/answerdotai/ModernBERT-large) |
|
on the [ymoslem/wmt-da-human-evaluation](https://huggingface.co/ymoslem/wmt-da-human-evaluation) dataset. |
|
|
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.0564 |
|
|
|
## Model description |
|
|
|
This model is for reference-free quality estimation (QE) of machine translation (MT) systems. |
|
|
|
## Training procedure |
|
|
|
### Training hyperparameters |
|
|
|
This model uses the full maximum length of the tokenizer, which is 8192. |
|
The version with 512 maximum length can be found here [ymoslem/ModernBERT-large-qe-maxlen512-v1](https://huggingface.co/ymoslem/ModernBERT-large-qe-maxlen512-v1) |
|
|
|
The following hyperparameters were used during training: |
|
- learning_rate: 8e-05 |
|
- train_batch_size: 128 |
|
- eval_batch_size: 128 |
|
- seed: 42 |
|
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
|
- lr_scheduler_type: linear |
|
- training_steps: 10000 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | |
|
|:-------------:|:------:|:-----:|:---------------:| |
|
| 0.0631 | 0.1004 | 1000 | 0.0674 | |
|
| 0.0614 | 0.2007 | 2000 | 0.0599 | |
|
| 0.0578 | 0.3011 | 3000 | 0.0585 | |
|
| 0.0585 | 0.4015 | 4000 | 0.0579 | |
|
| 0.0568 | 0.5019 | 5000 | 0.0570 | |
|
| 0.057 | 0.6022 | 6000 | 0.0568 | |
|
| 0.0579 | 0.7026 | 7000 | 0.0567 | |
|
| 0.0573 | 0.8030 | 8000 | 0.0565 | |
|
| 0.0568 | 0.9033 | 9000 | 0.0564 | |
|
| 0.0571 | 1.0037 | 10000 | 0.0564 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.48.0 |
|
- Pytorch 2.4.1+cu124 |
|
- Datasets 3.2.0 |
|
- Tokenizers 0.21.0 |
|
|
|
## Inference |
|
|
|
1. Install the required libraries. |
|
|
|
```bash |
|
pip3 install --upgrade datasets accelerate transformers |
|
pip3 install --upgrade flash_attn triton |
|
``` |
|
|
|
2. Load the test dataset. |
|
|
|
```python |
|
from datasets import load_dataset |
|
|
|
test_dataset = load_dataset("ymoslem/wmt-da-human-evaluation", |
|
split="test", |
|
trust_remote_code=True |
|
) |
|
print(test_dataset) |
|
``` |
|
|
|
3. Load the model and tokenizer: |
|
|
|
```python |
|
from transformers import AutoModelForSequenceClassification, AutoTokenizer |
|
import torch |
|
|
|
# Load the fine-tuned model and tokenizer |
|
model_name = "ymoslem/ModernBERT-large-qe-v1" |
|
model = AutoModelForSequenceClassification.from_pretrained( |
|
model_name, |
|
device_map="auto", |
|
torch_dtype=torch.bfloat16, |
|
attn_implementation="flash_attention_2", |
|
) |
|
tokenizer = AutoTokenizer.from_pretrained(model_name) |
|
|
|
# Move model to GPU if available |
|
device = "cuda" if torch.cuda.is_available() else "cpu" |
|
model.to(device) |
|
model.eval() |
|
``` |
|
|
|
4. Prepare the dataset. Each source segment `src` and target segment `tgt` are separated by the `sep_token`, which is `'</s>'` for ModernBERT. |
|
|
|
```python |
|
sep_token = tokenizer.sep_token |
|
input_test_texts = [f"{src} {sep_token} {tgt}" for src, tgt in zip(test_dataset["src"], test_dataset["mt"])] |
|
``` |
|
|
|
5. Generate predictions. |
|
|
|
If you print `model.config.problem_type`, the output is `regression`. |
|
Still, you can use the "text-classification" pipeline as follows (cf. [pipeline documentation](https://huggingface.co/docs/transformers/en/main_classes/pipelines#transformers.TextClassificationPipeline)): |
|
|
|
```python |
|
from transformers import pipeline |
|
|
|
classifier = pipeline("text-classification", |
|
model=model_name, |
|
tokenizer=tokenizer, |
|
device=0, |
|
) |
|
|
|
predictions = classifier(input_test_texts, |
|
batch_size=128, |
|
truncation=True, |
|
padding="max_length", |
|
max_length=tokenizer.model_max_length, |
|
) |
|
predictions = [prediction["score"] for prediction in predictions] |
|
|
|
``` |
|
|
|
Alternatively, you can use an elaborate version of the code, which is slightly faster and provides more control. |
|
|
|
```python |
|
from torch.utils.data import DataLoader |
|
import torch |
|
from tqdm.auto import tqdm |
|
|
|
# Tokenization function |
|
def process_batch(batch, tokenizer, device): |
|
sep_token = tokenizer.sep_token |
|
input_texts = [f"{src} {sep_token} {tgt}" for src, tgt in zip(batch["src"], batch["mt"])] |
|
tokens = tokenizer(input_texts, |
|
truncation=True, |
|
padding="max_length", |
|
max_length=tokenizer.model_max_length, |
|
return_tensors="pt", |
|
).to(device) |
|
return tokens |
|
|
|
|
|
|
|
# Create a DataLoader for batching |
|
test_dataloader = DataLoader(test_dataset, |
|
batch_size=128, # Adjust batch size as needed |
|
shuffle=False) |
|
|
|
|
|
# List to store all predictions |
|
predictions = [] |
|
|
|
with torch.no_grad(): |
|
for batch in tqdm(test_dataloader, desc="Inference Progress", unit="batch"): |
|
|
|
tokens = process_batch(batch, tokenizer, device) |
|
|
|
# Forward pass: Generate model's logits |
|
outputs = model(**tokens) |
|
|
|
# Get logits (predictions) |
|
logits = outputs.logits |
|
|
|
# Extract the regression predicted values |
|
batch_predictions = logits.squeeze() |
|
|
|
# Extend the list with the predictions |
|
predictions.extend(batch_predictions.tolist()) |
|
``` |
