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---
license: mit
base_model: FacebookAI/roberta-base
tags:
- generated_from_trainer
datasets:
- patent-classification
metrics:
- accuracy
model-index:
- name: roberta_base_patent
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: patent-classification
type: patent-classification
config: abstract
split: validation
args: abstract
metrics:
- name: Accuracy
type: accuracy
value: 0.679
---
<!-- 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. -->
# roberta_base_patent
This model is a fine-tuned version of [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) on the patent-classification dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9374
- Accuracy: 0.679
- F1 Macro: 0.6098
- F1 Micro: 0.679
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:--------:|
| 1.5014 | 0.13 | 50 | 1.4249 | 0.5094 | 0.3519 | 0.5094 |
| 1.2516 | 0.26 | 100 | 1.2353 | 0.5716 | 0.4110 | 0.5716 |
| 1.2231 | 0.38 | 150 | 1.1279 | 0.6184 | 0.4706 | 0.6184 |
| 1.1169 | 0.51 | 200 | 1.0773 | 0.6346 | 0.5016 | 0.6346 |
| 1.1195 | 0.64 | 250 | 1.0686 | 0.6398 | 0.5182 | 0.6398 |
| 1.0737 | 0.77 | 300 | 1.0548 | 0.6426 | 0.5232 | 0.6426 |
| 1.0981 | 0.9 | 350 | 1.0438 | 0.6376 | 0.5605 | 0.6376 |
| 1.0147 | 1.02 | 400 | 0.9970 | 0.6606 | 0.5852 | 0.6606 |
| 0.9049 | 1.15 | 450 | 1.0098 | 0.6572 | 0.5804 | 0.6572 |
| 0.945 | 1.28 | 500 | 0.9907 | 0.662 | 0.5873 | 0.662 |
| 0.9206 | 1.41 | 550 | 0.9865 | 0.6636 | 0.5777 | 0.6636 |
| 0.9263 | 1.53 | 600 | 0.9686 | 0.6664 | 0.5968 | 0.6664 |
| 0.9629 | 1.66 | 650 | 0.9791 | 0.666 | 0.5941 | 0.666 |
| 0.8913 | 1.79 | 700 | 0.9579 | 0.6746 | 0.6002 | 0.6746 |
| 0.96 | 1.92 | 750 | 0.9524 | 0.6696 | 0.6025 | 0.6696 |
| 0.8284 | 2.05 | 800 | 0.9540 | 0.6738 | 0.6073 | 0.6738 |
| 0.8558 | 2.17 | 850 | 0.9496 | 0.6742 | 0.5949 | 0.6742 |
| 0.9643 | 2.3 | 900 | 0.9520 | 0.6748 | 0.6074 | 0.6748 |
| 0.873 | 2.43 | 950 | 0.9521 | 0.6732 | 0.5963 | 0.6732 |
| 0.8271 | 2.56 | 1000 | 0.9399 | 0.6782 | 0.6123 | 0.6782 |
| 0.7572 | 2.69 | 1050 | 0.9400 | 0.6788 | 0.6076 | 0.6788 |
| 0.7949 | 2.81 | 1100 | 0.9386 | 0.6808 | 0.6099 | 0.6808 |
| 0.8183 | 2.94 | 1150 | 0.9374 | 0.679 | 0.6098 | 0.679 |
### Framework versions
- Transformers 4.39.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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