---
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: 
    - name: MAE
      type: Mean Absolute Error
      value: 
    - name: RMSE
      type: Root Mean Squared Error
      value: 
    - name: R-R2
      type: R-Squared
      value: 
metrics:
- pearsonr
- mae
- r_squared
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# 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/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](https://huggingface.co/ymoslem/ModernBERT-base-long-context-qe-v1),
which is trained on a long-context / document-level QE dataset [ymoslem/wmt-da-human-evaluation-long-context](https://huggingface.co/datasets/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](https://huggingface.co/datasets/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](https://huggingface.co/datasets/ymoslem/wmt-da-human-evaluation)

The long-context / document-level model can be found at: [ModernBERT-base-long-context-qe-v1](https://huggingface.co/ymoslem/ModernBERT-base-long-context-qe-v1),
which is trained on a long-context / document-level QE dataset [ymoslem/wmt-da-human-evaluation-long-context](https://huggingface.co/datasets/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