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eternis_router_encoder_sft_5Sep

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@@ -16,9 +16,16 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.2443
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- - Mse: 0.2443
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- - Model Accuracy: 0.2018
 
 
 
 
 
 
 
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  ## Model description
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@@ -37,29 +44,42 @@ More information needed
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 1e-05
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  - train_batch_size: 16
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- - eval_batch_size: 16
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  - seed: 42
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  - gradient_accumulation_steps: 2
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  - total_train_batch_size: 32
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  - optimizer: Use 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: cosine
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  - lr_scheduler_warmup_ratio: 0.06
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- - num_epochs: 3
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Mse | Model Accuracy |
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- |:-------------:|:------:|:----:|:---------------:|:------:|:--------------:|
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- | 1.432 | 0.3429 | 300 | 0.6756 | 0.6756 | 0.031 |
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- | 0.7353 | 0.6857 | 600 | 0.3514 | 0.3514 | 0.0825 |
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- | 0.541 | 1.0286 | 900 | 0.2742 | 0.2742 | 0.1725 |
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- | 0.5159 | 1.3714 | 1200 | 0.2563 | 0.2563 | 0.1578 |
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- | 0.4806 | 1.7143 | 1500 | 0.2495 | 0.2495 | 0.1805 |
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- | 0.4732 | 2.0571 | 1800 | 0.2462 | 0.2462 | 0.2035 |
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- | 0.4781 | 2.4 | 2100 | 0.2447 | 0.2447 | 0.1988 |
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- | 0.466 | 2.7429 | 2400 | 0.2443 | 0.2443 | 0.2018 |
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.1954
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+ - Mse: 0.1954
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+ - Mae: 0.1976
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+ - Vector Accuracy: 0.2235
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+ - Complexity Accuracy: 0.8013
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+ - Accuracy Accuracy: 0.9885
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+ - Completeness Accuracy: 0.9928
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+ - Clarity Accuracy: 0.997
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+ - Relevance Accuracy: 0.9978
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+ - Model Accuracy: 0.2898
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  ## Model description
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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+ - learning_rate: 0.002
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  - train_batch_size: 16
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+ - eval_batch_size: 32
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  - seed: 42
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  - gradient_accumulation_steps: 2
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  - total_train_batch_size: 32
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  - optimizer: Use 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: cosine
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  - lr_scheduler_warmup_ratio: 0.06
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+ - num_epochs: 6
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Mse | Mae | Vector Accuracy | Complexity Accuracy | Accuracy Accuracy | Completeness Accuracy | Clarity Accuracy | Relevance Accuracy | Model Accuracy |
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+ |:-------------:|:------:|:----:|:---------------:|:------:|:------:|:---------------:|:-------------------:|:-----------------:|:---------------------:|:----------------:|:------------------:|:--------------:|
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
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  ### Framework versions