--- 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 `''` 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()) ```