metadata

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/tokenized-wmt-da-human-evaluation
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
            type: Pearson Correlation
            value: 0.4465
          - name: MAE
            type: Mean Absolute Error
            value: 0.126
          - name: RMSE
            type: Root Mean Squared Error
            value: 0.1623
          - name: R-R2
            type: R-Squared
            value: 0.0801
      - task:
          type: regression
        dataset:
          name: ymoslem/wmt-da-human-evaluation
          type: QE
        metrics:
          - name: Pearson
            type: Pearson Correlation
            value: null
          - name: MAE
            type: Mean Absolute Error
            value: null
          - name: RMSE
            type: Root Mean Squared Error
            value: null
          - name: R-R2
            type: R-Squared
            value: null
metrics:
  - pearsonr
  - mae
  - r_squared

Quality Estimation for Machine Translation

This model is a fine-tuned version of answerdotai/ModernBERT-base on the ymoslem/tokenized-wmt-da-human-evaluation dataset. It achieves the following results on the evaluation set:

Loss: 0.0571

Model description

This model is for reference-free, sentence level quality estimation (QE) of machine translation (MT) systems. The long-context / document-level model can be found at: ModernBERT-base-long-context-qe-v1, which is trained on a long-context / document-level QE dataset ymoslem/wmt-da-human-evaluation-long-context

Training and evaluation data

This model is trained on the sentence-level quality estimation dataset: ymoslem/wmt-da-human-evaluation

Training procedure

This version of the model uses the full lengthtokenizer.model_max_length=8192, but it is still trained on a sentence-level QE dataset ymoslem/wmt-da-human-evaluation

The long-context / document-level model can be found at: ModernBERT-base-long-context-qe-v1, which is trained on a long-context / document-level QE dataset ymoslem/wmt-da-human-evaluation-long-context

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: 10000

Training results

Training Loss	Epoch	Step	Validation Loss
0.0686	0.1004	1000	0.0712
0.0652	0.2007	2000	0.0687
0.0648	0.3011	3000	0.0623
0.0609	0.4015	4000	0.0600
0.0585	0.5019	5000	0.0603
0.0588	0.6022	6000	0.0589
0.0592	0.7026	7000	0.0581
0.0585	0.8030	8000	0.0574
0.0588	0.9033	9000	0.0572
0.0563	1.0037	10000	0.0571

Framework versions

Transformers 4.48.1
Pytorch 2.4.1+cu124
Datasets 3.2.0
Tokenizers 0.21.0