--- 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: mit base_model: FacebookAI/xlm-roberta-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: 0.422 - name: Mean Absolute Error type: MAE value: 0.196 - name: Root Mean Squared Error type: RMSE value: 0.245 - name: R-Squared type: R2 value: 0.245 metrics: - perplexity - mae - r_squared --- # Quality Estimation for Machine Translation This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the ymoslem/wmt-da-human-evaluation dataset. It achieves the following results on the evaluation set: - Loss: 0.0752 ## Model description This model is for reference-free quality estimation (QE) of machine translation (MT) systems. ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 8e-05 - train_batch_size: 64 - eval_batch_size: 64 - 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: 20000 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:-----:|:---------------:| | 0.0743 | 0.0502 | 1000 | 0.0598 | | 0.0853 | 0.1004 | 2000 | 0.0745 | | 0.0829 | 0.1506 | 3000 | 0.0726 | | 0.0814 | 0.2008 | 4000 | 0.0872 | | 0.0805 | 0.2509 | 5000 | 0.0715 | | 0.0782 | 0.3011 | 6000 | 0.0819 | | 0.0789 | 0.3513 | 7000 | 0.0733 | | 0.0791 | 0.4015 | 8000 | 0.0748 | | 0.0787 | 0.4517 | 9000 | 0.0759 | | 0.0761 | 0.5019 | 10000 | 0.0725 | | 0.0746 | 0.5521 | 11000 | 0.0745 | | 0.0762 | 0.6023 | 12000 | 0.0750 | | 0.077 | 0.6524 | 13000 | 0.0725 | | 0.0777 | 0.7026 | 14000 | 0.0737 | | 0.0764 | 0.7528 | 15000 | 0.0745 | | 0.0781 | 0.8030 | 16000 | 0.0750 | | 0.0748 | 0.8532 | 17000 | 0.0765 | | 0.0768 | 0.9034 | 18000 | 0.0750 | | 0.0737 | 0.9536 | 19000 | 0.0759 | | 0.0769 | 1.0038 | 20000 | 0.0752 | ### 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 `''` 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()) ```