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--- |
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library_name: transformers |
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language: |
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- multilingual |
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- bn |
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- cs |
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- de |
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- en |
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- et |
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- fi |
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- fr |
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- gu |
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- ha |
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- hi |
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- is |
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- ja |
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- kk |
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- km |
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- lt |
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- lv |
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- pl |
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- ps |
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- ru |
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- ta |
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- tr |
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- uk |
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- xh |
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- zh |
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- zu |
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license: apache-2.0 |
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base_model: answerdotai/ModernBERT-base |
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tags: |
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- quality-estimation |
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- regression |
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- generated_from_trainer |
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datasets: |
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- ymoslem/wmt-da-human-evaluation-long-context |
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model-index: |
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- name: Quality Estimation for Machine Translation |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# Quality Estimation for Machine Translation |
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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. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0214 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0003 |
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- train_batch_size: 128 |
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- eval_batch_size: 128 |
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- seed: 42 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- training_steps: 60000 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:------:|:-----:|:---------------:| |
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| 0.0233 | 0.0167 | 1000 | 0.0233 | |
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| 0.0232 | 0.0335 | 2000 | 0.0230 | |
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| 0.0225 | 0.0502 | 3000 | 0.0230 | |
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| 0.023 | 0.0669 | 4000 | 0.0224 | |
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| 0.0226 | 0.0837 | 5000 | 0.0223 | |
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| 0.0226 | 0.1004 | 6000 | 0.0225 | |
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| 0.0219 | 0.1171 | 7000 | 0.0222 | |
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| 0.022 | 0.1339 | 8000 | 0.0222 | |
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| 0.0213 | 0.1506 | 9000 | 0.0221 | |
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| 0.0213 | 0.1673 | 10000 | 0.0220 | |
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| 0.0218 | 0.1840 | 11000 | 0.0219 | |
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| 0.0215 | 0.2008 | 12000 | 0.0225 | |
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| 0.0218 | 0.2175 | 13000 | 0.0219 | |
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| 0.0218 | 0.2342 | 14000 | 0.0218 | |
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| 0.0217 | 0.2510 | 15000 | 0.0219 | |
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| 0.0219 | 0.2677 | 16000 | 0.0219 | |
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| 0.0212 | 0.2844 | 17000 | 0.0219 | |
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| 0.0219 | 0.3012 | 18000 | 0.0219 | |
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| 0.0218 | 0.3179 | 19000 | 0.0219 | |
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| 0.0213 | 0.3346 | 20000 | 0.0217 | |
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| 0.0218 | 0.3514 | 21000 | 0.0217 | |
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| 0.021 | 0.3681 | 22000 | 0.0217 | |
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| 0.0219 | 0.3848 | 23000 | 0.0220 | |
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| 0.0211 | 0.4016 | 24000 | 0.0216 | |
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| 0.0211 | 0.4183 | 25000 | 0.0216 | |
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| 0.0206 | 0.4350 | 26000 | 0.0216 | |
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| 0.021 | 0.4517 | 27000 | 0.0215 | |
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| 0.0214 | 0.4685 | 28000 | 0.0215 | |
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| 0.0214 | 0.4852 | 29000 | 0.0216 | |
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| 0.0204 | 0.5019 | 30000 | 0.0216 | |
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| 0.022 | 0.5187 | 31000 | 0.0216 | |
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| 0.0212 | 0.5354 | 32000 | 0.0217 | |
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| 0.0211 | 0.5521 | 33000 | 0.0216 | |
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| 0.0208 | 0.5689 | 34000 | 0.0215 | |
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| 0.0208 | 0.5856 | 35000 | 0.0215 | |
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| 0.0215 | 0.6023 | 36000 | 0.0215 | |
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| 0.0212 | 0.6191 | 37000 | 0.0215 | |
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| 0.0213 | 0.6358 | 38000 | 0.0215 | |
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| 0.0211 | 0.6525 | 39000 | 0.0215 | |
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| 0.0208 | 0.6693 | 40000 | 0.0215 | |
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| 0.0205 | 0.6860 | 41000 | 0.0215 | |
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| 0.0209 | 0.7027 | 42000 | 0.0215 | |
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| 0.021 | 0.7194 | 43000 | 0.0215 | |
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| 0.0207 | 0.7362 | 44000 | 0.0215 | |
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| 0.0197 | 0.7529 | 45000 | 0.0215 | |
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| 0.0211 | 0.7696 | 46000 | 0.0214 | |
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| 0.021 | 0.7864 | 47000 | 0.0215 | |
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| 0.0207 | 0.8031 | 48000 | 0.0214 | |
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| 0.0219 | 0.8198 | 49000 | 0.0215 | |
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| 0.0208 | 0.8366 | 50000 | 0.0215 | |
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| 0.0202 | 0.8533 | 51000 | 0.0215 | |
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| 0.02 | 0.8700 | 52000 | 0.0215 | |
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| 0.0205 | 0.8868 | 53000 | 0.0214 | |
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| 0.0214 | 0.9035 | 54000 | 0.0215 | |
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| 0.0205 | 0.9202 | 55000 | 0.0214 | |
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| 0.0209 | 0.9370 | 56000 | 0.0214 | |
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| 0.0206 | 0.9537 | 57000 | 0.0214 | |
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| 0.0204 | 0.9704 | 58000 | 0.0214 | |
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| 0.0203 | 0.9872 | 59000 | 0.0214 | |
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| 0.0209 | 1.0039 | 60000 | 0.0214 | |
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### Framework versions |
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- Transformers 4.48.1 |
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- Pytorch 2.4.1+cu124 |
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- Datasets 3.2.0 |
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- Tokenizers 0.21.0 |
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