| | --- |
| | library_name: transformers |
| | language: |
| | - en |
| | license: apache-2.0 |
| | base_model: answerdotai/ModernBERT-base |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - precision |
| | - recall |
| | - f1 |
| | - accuracy |
| | model-index: |
| | - name: DisambertSingleSense-base |
| | 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. --> |
| |
|
| | # DisambertSingleSense-base |
| |
|
| | This model is a fine-tuned version of [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on the semcor dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 10.3845 |
| | - Precision: 0.9250 |
| | - Recall: 0.5786 |
| | - F1: 0.7119 |
| | - Accuracy: 0.6008 |
| | - Matthews: 0.6006 |
| |
|
| | ## 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: 0.0001 |
| | - train_batch_size: 16 |
| | - eval_batch_size: 16 |
| | - seed: 42 |
| | - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| | - lr_scheduler_type: inverse_sqrt |
| | - lr_scheduler_warmup_steps: 1000 |
| | - num_epochs: 30 |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | Matthews | |
| | |:-------------:|:-----:|:------:|:---------------:|:---------:|:------:|:------:|:--------:|:--------:| |
| | | No log | 0 | 0 | 207.0982 | 0.0 | 0.0 | 0.0 | 0.0 | -0.0000 | |
| | | 11.1217 | 1.0 | 14014 | 15.0481 | 0.9215 | 0.5209 | 0.6656 | 0.4562 | 0.4558 | |
| | | 5.9994 | 2.0 | 28028 | 10.3853 | 0.7928 | 0.3539 | 0.4894 | 0.4978 | 0.4979 | |
| | | 3.7236 | 3.0 | 42042 | 8.8450 | 0.9086 | 0.5679 | 0.6989 | 0.5570 | 0.5566 | |
| | | 2.5493 | 4.0 | 56056 | 8.7346 | 0.9313 | 0.5675 | 0.7053 | 0.5793 | 0.5790 | |
| | | 1.9121 | 5.0 | 70070 | 8.9990 | 0.9163 | 0.5669 | 0.7004 | 0.5701 | 0.5698 | |
| | | 0.9166 | 6.0 | 84084 | 9.2895 | 0.9287 | 0.5799 | 0.7139 | 0.5815 | 0.5812 | |
| | | 0.8231 | 7.0 | 98098 | 9.3043 | 0.9185 | 0.5844 | 0.7143 | 0.5907 | 0.5904 | |
| | | 0.4919 | 8.0 | 112112 | 9.7527 | 0.9216 | 0.5668 | 0.7019 | 0.5802 | 0.5799 | |
| | | 0.5579 | 9.0 | 126126 | 9.9372 | 0.9265 | 0.5745 | 0.7092 | 0.5929 | 0.5926 | |
| | | 0.3221 | 10.0 | 140140 | 10.1643 | 0.9254 | 0.5726 | 0.7074 | 0.5868 | 0.5865 | |
| | | 0.4007 | 11.0 | 154154 | 10.1666 | 0.9077 | 0.5722 | 0.7019 | 0.5885 | 0.5882 | |
| | | 0.1726 | 12.0 | 168168 | 10.3202 | 0.9179 | 0.5691 | 0.7026 | 0.5894 | 0.5891 | |
| | | 0.2729 | 13.0 | 182182 | 10.4281 | 0.9127 | 0.5648 | 0.6978 | 0.5916 | 0.5913 | |
| | | 0.1867 | 14.0 | 196196 | 10.3487 | 0.9042 | 0.5731 | 0.7016 | 0.5951 | 0.5948 | |
| | | 0.1512 | 15.0 | 210210 | 10.2347 | 0.9262 | 0.5742 | 0.7089 | 0.5968 | 0.5966 | |
| | | 0.1377 | 16.0 | 224224 | 10.3734 | 0.9211 | 0.5772 | 0.7097 | 0.6017 | 0.6014 | |
| | | 0.2627 | 17.0 | 238238 | 10.5554 | 0.9212 | 0.5767 | 0.7093 | 0.5990 | 0.5988 | |
| | | 0.1610 | 18.0 | 252252 | 10.4423 | 0.9273 | 0.5748 | 0.7097 | 0.6008 | 0.6006 | |
| | | 0.1973 | 19.0 | 266266 | 10.6396 | 0.9289 | 0.5729 | 0.7087 | 0.5947 | 0.5945 | |
| | | 0.1504 | 20.0 | 280280 | 10.5432 | 0.9132 | 0.5740 | 0.7049 | 0.5995 | 0.5992 | |
| | | 0.0363 | 21.0 | 294294 | 10.6388 | 0.9291 | 0.5744 | 0.7099 | 0.5986 | 0.5984 | |
| | | 0.0384 | 22.0 | 308308 | 10.5433 | 0.9314 | 0.5750 | 0.7111 | 0.5977 | 0.5975 | |
| | | 0.0792 | 23.0 | 322322 | 10.7152 | 0.9308 | 0.5752 | 0.7110 | 0.5995 | 0.5994 | |
| | | 0.0165 | 24.0 | 336336 | 10.6516 | 0.9301 | 0.5690 | 0.7061 | 0.5964 | 0.5962 | |
| | | 0.0644 | 25.0 | 350350 | 10.3666 | 0.9297 | 0.5788 | 0.7134 | 0.6012 | 0.6010 | |
| | | 0.0246 | 26.0 | 364364 | 10.3480 | 0.9285 | 0.5700 | 0.7064 | 0.5947 | 0.5945 | |
| | | 0.0518 | 27.0 | 378378 | 10.6784 | 0.9300 | 0.5783 | 0.7131 | 0.5977 | 0.5975 | |
| | | 0.0267 | 28.0 | 392392 | 10.7434 | 0.9306 | 0.5742 | 0.7102 | 0.5999 | 0.5998 | |
| | | 0.0847 | 29.0 | 406406 | 10.4787 | 0.9289 | 0.5787 | 0.7131 | 0.6017 | 0.6014 | |
| | | 0.0923 | 30.0 | 420420 | 10.3845 | 0.9250 | 0.5786 | 0.7119 | 0.6008 | 0.6006 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 5.1.0 |
| | - Pytorch 2.6.0+cu124 |
| | - Datasets 4.5.0 |
| | - Tokenizers 0.22.2 |
| |
|
