File size: 4,127 Bytes
c6beb4a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 |
---
library_name: transformers
license: apache-2.0
base_model: answerdotai/ModernBERT-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: modernBert-base_v2
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. -->
# modernBert-base_v2
This model is a fine-tuned version of [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7185
- Accuracy: 0.9116
- Precision Macro: 0.8041
- Recall Macro: 0.7362
- F1 Macro: 0.7592
- F1 Weighted: 0.9065
## 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: 3e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision Macro | Recall Macro | F1 Macro | F1 Weighted |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------------:|:------------:|:--------:|:-----------:|
| 1.2139 | 1.0 | 90 | 0.5024 | 0.8073 | 0.8182 | 0.5993 | 0.6061 | 0.7934 |
| 0.6774 | 2.0 | 180 | 0.2870 | 0.9033 | 0.8421 | 0.7140 | 0.7451 | 0.8960 |
| 0.4571 | 3.0 | 270 | 0.3474 | 0.8920 | 0.8074 | 0.6669 | 0.6824 | 0.8802 |
| 0.2925 | 4.0 | 360 | 0.3089 | 0.9065 | 0.8778 | 0.7074 | 0.7413 | 0.8977 |
| 0.1725 | 5.0 | 450 | 0.3611 | 0.8958 | 0.7729 | 0.7574 | 0.7646 | 0.8946 |
| 0.0977 | 6.0 | 540 | 0.4743 | 0.9090 | 0.8405 | 0.7388 | 0.7695 | 0.9036 |
| 0.0576 | 7.0 | 630 | 0.6044 | 0.8743 | 0.7234 | 0.8019 | 0.7413 | 0.8878 |
| 0.0338 | 8.0 | 720 | 0.6118 | 0.9040 | 0.7756 | 0.7506 | 0.7615 | 0.9019 |
| 0.016 | 9.0 | 810 | 0.6754 | 0.9071 | 0.8334 | 0.7379 | 0.7670 | 0.9019 |
| 0.0113 | 10.0 | 900 | 0.6732 | 0.9065 | 0.7898 | 0.7606 | 0.7733 | 0.9044 |
| 0.0065 | 11.0 | 990 | 0.7871 | 0.9046 | 0.8046 | 0.7277 | 0.7519 | 0.8992 |
| 0.0037 | 12.0 | 1080 | 0.7134 | 0.9109 | 0.7989 | 0.7147 | 0.7386 | 0.9038 |
| 0.0022 | 13.0 | 1170 | 0.7784 | 0.9015 | 0.7765 | 0.7383 | 0.7529 | 0.8982 |
| 0.0013 | 14.0 | 1260 | 0.7176 | 0.9109 | 0.7832 | 0.7486 | 0.7625 | 0.9079 |
| 0.0011 | 15.0 | 1350 | 0.7681 | 0.9059 | 0.7920 | 0.7371 | 0.7565 | 0.9017 |
| 0.0001 | 16.0 | 1440 | 0.7170 | 0.9071 | 0.7833 | 0.7282 | 0.7479 | 0.9024 |
| 0.0007 | 17.0 | 1530 | 0.7219 | 0.9109 | 0.8022 | 0.7442 | 0.7652 | 0.9068 |
| 0.0003 | 18.0 | 1620 | 0.7379 | 0.9103 | 0.7950 | 0.7398 | 0.7596 | 0.9060 |
| 0.0006 | 19.0 | 1710 | 0.7198 | 0.9116 | 0.8074 | 0.7404 | 0.7635 | 0.9068 |
| 0.0004 | 20.0 | 1800 | 0.7185 | 0.9116 | 0.8041 | 0.7362 | 0.7592 | 0.9065 |
### Framework versions
- Transformers 4.55.0
- Pytorch 2.7.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4
|