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---
library_name: transformers
base_model: KennethEnevoldsen/dfm-sentence-encoder-large-exp2-no-lang-align
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: dfm1
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. -->
# dfm1
This model is a fine-tuned version of [KennethEnevoldsen/dfm-sentence-encoder-large-exp2-no-lang-align](https://huggingface.co/KennethEnevoldsen/dfm-sentence-encoder-large-exp2-no-lang-align) on an unknown dataset.
It achieves the following results on the evaluation set:
- Accuracy: 0.8868
- Precision: 0.8861
- Recall: 0.8868
- F1: 0.8855
- Loss: 0.5432
## 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: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Accuracy | Precision | Recall | F1 | Validation Loss |
|:-------------:|:-------:|:----:|:--------:|:---------:|:------:|:------:|:---------------:|
| No log | 0.9412 | 8 | 0.7844 | 0.7464 | 0.7844 | 0.7612 | 0.7436 |
| No log | 2.0 | 17 | 0.8999 | 0.8922 | 0.8999 | 0.8914 | 0.3252 |
| No log | 2.9412 | 25 | 0.9214 | 0.9226 | 0.9214 | 0.9121 | 0.3213 |
| No log | 4.0 | 34 | 0.9164 | 0.9235 | 0.9164 | 0.9176 | 0.3572 |
| No log | 4.9412 | 42 | 0.8880 | 0.8875 | 0.8880 | 0.8857 | 0.3576 |
| No log | 6.0 | 51 | 0.8907 | 0.8894 | 0.8907 | 0.8898 | 0.3993 |
| No log | 6.9412 | 59 | 0.8822 | 0.8822 | 0.8822 | 0.8806 | 0.4444 |
| No log | 8.0 | 68 | 0.8876 | 0.8867 | 0.8876 | 0.8865 | 0.4480 |
| No log | 8.9412 | 76 | 0.8987 | 0.8978 | 0.8987 | 0.8979 | 0.4688 |
| No log | 10.0 | 85 | 0.8984 | 0.8972 | 0.8984 | 0.8975 | 0.4845 |
| No log | 10.9412 | 93 | 0.8895 | 0.8887 | 0.8895 | 0.8884 | 0.5172 |
| No log | 12.0 | 102 | 0.8891 | 0.8882 | 0.8891 | 0.8881 | 0.5349 |
| No log | 12.9412 | 110 | 0.8907 | 0.8897 | 0.8907 | 0.8896 | 0.5343 |
| No log | 14.0 | 119 | 0.8895 | 0.8886 | 0.8895 | 0.8884 | 0.5374 |
| No log | 14.9412 | 127 | 0.8868 | 0.8861 | 0.8868 | 0.8855 | 0.5317 |
| No log | 16.0 | 136 | 0.8853 | 0.8847 | 0.8853 | 0.8839 | 0.5383 |
| No log | 16.9412 | 144 | 0.8853 | 0.8847 | 0.8853 | 0.8839 | 0.5402 |
| No log | 18.0 | 153 | 0.8865 | 0.8858 | 0.8865 | 0.8851 | 0.5429 |
| No log | 18.8235 | 160 | 0.8868 | 0.8861 | 0.8868 | 0.8855 | 0.5432 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.5.0+cu121
- Tokenizers 0.19.1
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