|
--- |
|
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-base |
|
tags: |
|
- quality-estimation |
|
- regression |
|
- generated_from_trainer |
|
datasets: |
|
- ymoslem/wmt-da-human-evaluation-long-context |
|
model-index: |
|
- name: Quality Estimation for Machine Translation |
|
results: |
|
- task: |
|
type: regression |
|
dataset: |
|
name: ymoslem/wmt-da-human-evaluation-long-context |
|
type: QE |
|
metrics: |
|
- name: Pearson Correlation |
|
type: Pearson |
|
value: 0.5013 |
|
- name: Mean Absolute Error |
|
type: MAE |
|
value: 0.1024 |
|
- name: Root Mean Squared Error |
|
type: RMSE |
|
value: 0.1464 |
|
- name: R-Squared |
|
type: R2 |
|
value: 0.251 |
|
metrics: |
|
- pearsonr |
|
- mae |
|
- r_squared |
|
--- |
|
|
|
# Quality Estimation for Machine Translation |
|
|
|
This model is a fine-tuned version of [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on the ymoslem/wmt-da-human-evaluation-long-context dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.0214 |
|
- Pearson: 0.5013 |
|
- MAE: 0.1024 |
|
- RMSE: 0.1464 |
|
- R2: 0.251 |
|
|
|
## Model description |
|
|
|
This model is for reference-free, long-context quality estimation (QE) of machine translation (MT) systems. |
|
It is trained on a dataset of translation pairs comprising up to 32 sentences (64 sentences for the source and target). |
|
Hence, this model is suitable for document-level quality estimation. |
|
|
|
## Training and evaluation data |
|
|
|
The model is trained on the long-context dataset [ymoslem/wmt-da-human-evaluation-long-context](https://huggingface.co/datasets/ymoslem/wmt-da-human-evaluation-long-context). |
|
The used long-context / document-level dataset for Quality Estimation of Machine Translation is an augmented variant of the sentence-level WMT DA Human Evaluation dataset. |
|
In addition to individual sentences, it contains augmentations of 2, 4, 8, 16, and 32 sentences, among each language pair `lp` and `domain`. |
|
The `raw` column represents a weighted average of scores of augmented sentences using character lengths of `src` and `mt` as weights. |
|
|
|
* Training data: 7.65 million long-context texts |
|
* Test data: 59,235 long-context texts |
|
|
|
## Training procedure |
|
|
|
The model is trained on 1x H200 SXM (143 GB VRAM) for approx. 26 hours. |
|
|
|
- tokenizer.model_max_length: 8192 (full context length) |
|
- attn_implementation: flash_attention_2 |
|
|
|
### Training hyperparameters |
|
|
|
The following hyperparameters were used during training: |
|
- learning_rate: 0.0003 |
|
- 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: 60000 (approx. 1 epoch) |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | |
|
|:-------------:|:------:|:-----:|:---------------:| |
|
| 0.0233 | 0.0167 | 1000 | 0.0233 | |
|
| 0.0232 | 0.0335 | 2000 | 0.0230 | |
|
| 0.0225 | 0.0502 | 3000 | 0.0230 | |
|
| 0.023 | 0.0669 | 4000 | 0.0224 | |
|
| 0.0226 | 0.0837 | 5000 | 0.0223 | |
|
| 0.0226 | 0.1004 | 6000 | 0.0225 | |
|
| 0.0219 | 0.1171 | 7000 | 0.0222 | |
|
| 0.022 | 0.1339 | 8000 | 0.0222 | |
|
| 0.0213 | 0.1506 | 9000 | 0.0221 | |
|
| 0.0213 | 0.1673 | 10000 | 0.0220 | |
|
| 0.0218 | 0.1840 | 11000 | 0.0219 | |
|
| 0.0215 | 0.2008 | 12000 | 0.0225 | |
|
| 0.0218 | 0.2175 | 13000 | 0.0219 | |
|
| 0.0218 | 0.2342 | 14000 | 0.0218 | |
|
| 0.0217 | 0.2510 | 15000 | 0.0219 | |
|
| 0.0219 | 0.2677 | 16000 | 0.0219 | |
|
| 0.0212 | 0.2844 | 17000 | 0.0219 | |
|
| 0.0219 | 0.3012 | 18000 | 0.0219 | |
|
| 0.0218 | 0.3179 | 19000 | 0.0219 | |
|
| 0.0213 | 0.3346 | 20000 | 0.0217 | |
|
| 0.0218 | 0.3514 | 21000 | 0.0217 | |
|
| 0.021 | 0.3681 | 22000 | 0.0217 | |
|
| 0.0219 | 0.3848 | 23000 | 0.0220 | |
|
| 0.0211 | 0.4016 | 24000 | 0.0216 | |
|
| 0.0211 | 0.4183 | 25000 | 0.0216 | |
|
| 0.0206 | 0.4350 | 26000 | 0.0216 | |
|
| 0.021 | 0.4517 | 27000 | 0.0215 | |
|
| 0.0214 | 0.4685 | 28000 | 0.0215 | |
|
| 0.0214 | 0.4852 | 29000 | 0.0216 | |
|
| 0.0204 | 0.5019 | 30000 | 0.0216 | |
|
| 0.022 | 0.5187 | 31000 | 0.0216 | |
|
| 0.0212 | 0.5354 | 32000 | 0.0217 | |
|
| 0.0211 | 0.5521 | 33000 | 0.0216 | |
|
| 0.0208 | 0.5689 | 34000 | 0.0215 | |
|
| 0.0208 | 0.5856 | 35000 | 0.0215 | |
|
| 0.0215 | 0.6023 | 36000 | 0.0215 | |
|
| 0.0212 | 0.6191 | 37000 | 0.0215 | |
|
| 0.0213 | 0.6358 | 38000 | 0.0215 | |
|
| 0.0211 | 0.6525 | 39000 | 0.0215 | |
|
| 0.0208 | 0.6693 | 40000 | 0.0215 | |
|
| 0.0205 | 0.6860 | 41000 | 0.0215 | |
|
| 0.0209 | 0.7027 | 42000 | 0.0215 | |
|
| 0.021 | 0.7194 | 43000 | 0.0215 | |
|
| 0.0207 | 0.7362 | 44000 | 0.0215 | |
|
| 0.0197 | 0.7529 | 45000 | 0.0215 | |
|
| 0.0211 | 0.7696 | 46000 | 0.0214 | |
|
| 0.021 | 0.7864 | 47000 | 0.0215 | |
|
| 0.0207 | 0.8031 | 48000 | 0.0214 | |
|
| 0.0219 | 0.8198 | 49000 | 0.0215 | |
|
| 0.0208 | 0.8366 | 50000 | 0.0215 | |
|
| 0.0202 | 0.8533 | 51000 | 0.0215 | |
|
| 0.02 | 0.8700 | 52000 | 0.0215 | |
|
| 0.0205 | 0.8868 | 53000 | 0.0214 | |
|
| 0.0214 | 0.9035 | 54000 | 0.0215 | |
|
| 0.0205 | 0.9202 | 55000 | 0.0214 | |
|
| 0.0209 | 0.9370 | 56000 | 0.0214 | |
|
| 0.0206 | 0.9537 | 57000 | 0.0214 | |
|
| 0.0204 | 0.9704 | 58000 | 0.0214 | |
|
| 0.0203 | 0.9872 | 59000 | 0.0214 | |
|
| 0.0209 | 1.0039 | 60000 | 0.0214 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.48.1 |
|
- 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-base-long-context-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()) |
|
``` |
|
|
|
|
