| | --- |
| | library_name: transformers |
| | license: apache-2.0 |
| | base_model: answerdotai/ModernBERT-base |
| | tags: |
| | - generated_from_trainer |
| | model-index: |
| | - name: eternis_router_encoder_sft_5Sep |
| | 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. --> |
| |
|
| | # eternis_router_encoder_sft_5Sep |
| |
|
| | This model is a fine-tuned version of [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on the None dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.1954 |
| | - Mse: 0.1954 |
| | - Mae: 0.1976 |
| | - Vector Accuracy: 0.2235 |
| | - Complexity Accuracy: 0.8013 |
| | - Accuracy Accuracy: 0.9885 |
| | - Completeness Accuracy: 0.9928 |
| | - Clarity Accuracy: 0.997 |
| | - Relevance Accuracy: 0.9978 |
| | - Model Accuracy: 0.2898 |
| |
|
| | ## 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: 0.002 |
| | - train_batch_size: 16 |
| | - eval_batch_size: 32 |
| | - seed: 42 |
| | - gradient_accumulation_steps: 2 |
| | - total_train_batch_size: 32 |
| | - optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| | - lr_scheduler_type: cosine |
| | - lr_scheduler_warmup_ratio: 0.06 |
| | - num_epochs: 6 |
| | |
| | ### Training results |
| | |
| | | Training Loss | Epoch | Step | Validation Loss | Mse | Mae | Vector Accuracy | Complexity Accuracy | Accuracy Accuracy | Completeness Accuracy | Clarity Accuracy | Relevance Accuracy | Model Accuracy | |
| | |:-------------:|:------:|:----:|:---------------:|:------:|:------:|:---------------:|:-------------------:|:-----------------:|:---------------------:|:----------------:|:------------------:|:--------------:| |
| | | 0.425 | 0.2857 | 250 | 0.2167 | 0.2167 | 0.2256 | 0.164 | 0.7642 | 0.9885 | 0.9928 | 0.997 | 0.9978 | 0.2157 | |
| | | 0.4162 | 0.5714 | 500 | 0.2129 | 0.2129 | 0.2096 | 0.2405 | 0.7745 | 0.9885 | 0.9928 | 0.997 | 0.9978 | 0.3235 | |
| | | 0.3955 | 0.8571 | 750 | 0.2135 | 0.2135 | 0.2140 | 0.1708 | 0.782 | 0.9885 | 0.9928 | 0.997 | 0.9978 | 0.246 | |
| | | 0.3864 | 1.1429 | 1000 | 0.2014 | 0.2014 | 0.2046 | 0.195 | 0.8035 | 0.9885 | 0.9928 | 0.997 | 0.9978 | 0.254 | |
| | | 0.4043 | 1.4286 | 1250 | 0.2029 | 0.2029 | 0.2086 | 0.1893 | 0.806 | 0.9885 | 0.9928 | 0.997 | 0.9978 | 0.2507 | |
| | | 0.3942 | 1.7143 | 1500 | 0.2046 | 0.2046 | 0.2022 | 0.233 | 0.804 | 0.9885 | 0.9928 | 0.997 | 0.9978 | 0.2935 | |
| | | 0.3952 | 2.0 | 1750 | 0.2103 | 0.2103 | 0.2196 | 0.1762 | 0.721 | 0.9885 | 0.9928 | 0.997 | 0.9978 | 0.2622 | |
| | | 0.3929 | 2.2857 | 2000 | 0.2011 | 0.2011 | 0.2014 | 0.2305 | 0.788 | 0.9885 | 0.9928 | 0.997 | 0.9978 | 0.3023 | |
| | | 0.3921 | 2.5714 | 2250 | 0.1986 | 0.1986 | 0.2019 | 0.2258 | 0.7778 | 0.9885 | 0.9928 | 0.997 | 0.9978 | 0.3045 | |
| | | 0.3924 | 2.8571 | 2500 | 0.1981 | 0.1981 | 0.1980 | 0.235 | 0.8043 | 0.9885 | 0.9928 | 0.997 | 0.9978 | 0.2988 | |
| | | 0.3819 | 3.1429 | 2750 | 0.2035 | 0.2035 | 0.2084 | 0.218 | 0.7638 | 0.9885 | 0.9928 | 0.997 | 0.9978 | 0.294 | |
| | | 0.3874 | 3.4286 | 3000 | 0.1970 | 0.1970 | 0.1963 | 0.2233 | 0.8073 | 0.9885 | 0.9928 | 0.997 | 0.9978 | 0.286 | |
| | | 0.3934 | 3.7143 | 3250 | 0.1994 | 0.1994 | 0.2079 | 0.184 | 0.786 | 0.9885 | 0.9928 | 0.997 | 0.9978 | 0.2487 | |
| | | 0.3813 | 4.0 | 3500 | 0.1985 | 0.1985 | 0.1942 | 0.245 | 0.8005 | 0.9885 | 0.9928 | 0.997 | 0.9978 | 0.314 | |
| | | 0.3939 | 4.2857 | 3750 | 0.1986 | 0.1986 | 0.2017 | 0.1905 | 0.8033 | 0.9885 | 0.9928 | 0.997 | 0.9978 | 0.2507 | |
| | | 0.3985 | 4.5714 | 4000 | 0.1956 | 0.1956 | 0.1993 | 0.2062 | 0.797 | 0.9885 | 0.9928 | 0.997 | 0.9978 | 0.273 | |
| | | 0.378 | 4.8571 | 4250 | 0.1960 | 0.1960 | 0.1991 | 0.227 | 0.7887 | 0.9885 | 0.9928 | 0.997 | 0.9978 | 0.2983 | |
| | | 0.3853 | 5.1429 | 4500 | 0.1957 | 0.1957 | 0.1982 | 0.2122 | 0.803 | 0.9885 | 0.9928 | 0.997 | 0.9978 | 0.2747 | |
| | | 0.3727 | 5.4286 | 4750 | 0.1955 | 0.1955 | 0.1989 | 0.2122 | 0.8025 | 0.9885 | 0.9928 | 0.997 | 0.9978 | 0.2745 | |
| | | 0.3826 | 5.7143 | 5000 | 0.1956 | 0.1956 | 0.1975 | 0.2278 | 0.8007 | 0.9885 | 0.9928 | 0.997 | 0.9978 | 0.2945 | |
| | | 0.3746 | 6.0 | 5250 | 0.1954 | 0.1954 | 0.1976 | 0.2235 | 0.8013 | 0.9885 | 0.9928 | 0.997 | 0.9978 | 0.2898 | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.56.1 |
| | - Pytorch 2.8.0+cu128 |
| | - Datasets 4.0.0 |
| | - Tokenizers 0.22.0 |
| | |
