---
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: []
---

<!-- 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/wmt-da-human-evaluation-long-context dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0214

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### 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

### 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