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---
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert_base_uncased_patent
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. -->
# distilbert_base_uncased_patent
This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9811
- Accuracy: 0.6632
- F1 Macro: 0.5701
- F1 Micro: 0.6632
## 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: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 64
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | F1 Micro |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:--------:|
| 1.5572 | 0.13 | 50 | 1.4884 | 0.504 | 0.3171 | 0.504 |
| 1.2925 | 0.26 | 100 | 1.2877 | 0.5634 | 0.3803 | 0.5634 |
| 1.253 | 0.38 | 150 | 1.2014 | 0.5974 | 0.4162 | 0.5974 |
| 1.1591 | 0.51 | 200 | 1.1558 | 0.6102 | 0.4468 | 0.6102 |
| 1.1756 | 0.64 | 250 | 1.1151 | 0.6244 | 0.4725 | 0.6244 |
| 1.1078 | 0.77 | 300 | 1.1123 | 0.6268 | 0.4912 | 0.6268 |
| 1.1463 | 0.9 | 350 | 1.0832 | 0.627 | 0.5030 | 0.627 |
| 1.0328 | 1.02 | 400 | 1.0610 | 0.6432 | 0.5068 | 0.6432 |
| 0.9224 | 1.15 | 450 | 1.0462 | 0.6476 | 0.5153 | 0.6476 |
| 0.9902 | 1.28 | 500 | 1.0401 | 0.6448 | 0.5168 | 0.6448 |
| 0.9681 | 1.41 | 550 | 1.0253 | 0.6546 | 0.5216 | 0.6546 |
| 0.9657 | 1.53 | 600 | 1.0123 | 0.6564 | 0.5248 | 0.6564 |
| 0.9742 | 1.66 | 650 | 1.0186 | 0.656 | 0.5263 | 0.656 |
| 0.9443 | 1.79 | 700 | 1.0028 | 0.66 | 0.5279 | 0.66 |
| 0.9944 | 1.92 | 750 | 1.0000 | 0.6544 | 0.5324 | 0.6544 |
| 0.849 | 2.05 | 800 | 0.9939 | 0.6588 | 0.5571 | 0.6588 |
| 0.8801 | 2.17 | 850 | 0.9916 | 0.6608 | 0.5618 | 0.6608 |
| 0.9913 | 2.3 | 900 | 0.9912 | 0.6634 | 0.5686 | 0.6634 |
| 0.923 | 2.43 | 950 | 0.9879 | 0.666 | 0.5739 | 0.666 |
| 0.8935 | 2.56 | 1000 | 0.9828 | 0.6642 | 0.5695 | 0.6642 |
| 0.8062 | 2.69 | 1050 | 0.9877 | 0.6598 | 0.5691 | 0.6598 |
| 0.853 | 2.81 | 1100 | 0.9811 | 0.6632 | 0.5701 | 0.6632 |
| 0.8978 | 2.94 | 1150 | 0.9811 | 0.6638 | 0.5709 | 0.6638 |
### Framework versions
- Transformers 4.39.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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