---
license: apache-2.0
base_model: anferico/bert-for-patents
tags:
- generated_from_keras_callback
model-index:
- name: BERT_for_Patents_PatentAbstract2IncomeGroup_2500
  results: []
---

<!-- This model card has been generated automatically according to the information Keras had access to. You should
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# BERT_for_Patents_PatentAbstract2IncomeGroup_2500

This model is a fine-tuned version of [anferico/bert-for-patents](https://huggingface.co/anferico/bert-for-patents) on a small subset (2500 samples) of the Google Patents Public Dataset. 
It uses patent abstracts to predict the income group of the country that has filed the patent. 
This is a proof-of-concept for a future text classification task.

It achieves the following results on the evaluation set:
- Train Loss: 0.0345
- Validation Loss: 0.3008
- Train Accuracy: 0.9028
- Epoch: 3

## 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:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 448, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32

### Training results

| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.4100     | 0.3570          | 0.8401         | 0     |
| 0.2116     | 0.2951          | 0.8683         | 1     |
| 0.0859     | 0.2870          | 0.8934         | 2     |
| 0.0345     | 0.3008          | 0.9028         | 3     |


### Framework versions

- Transformers 4.31.0
- TensorFlow 2.12.0
- Datasets 2.14.0
- Tokenizers 0.13.3