---
license: apache-2.0
base_model: google-bert/bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert_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. -->

# bert_base_uncased_patent

This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9466
- Accuracy: 0.6778
- F1 Macro: 0.6087
- F1 Micro: 0.6778

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:--------:|
| 1.6046        | 0.13  | 50   | 1.5103          | 0.5066   | 0.3622   | 0.5066   |
| 1.3113        | 0.26  | 100  | 1.2755          | 0.5716   | 0.3978   | 0.5716   |
| 1.2453        | 0.38  | 150  | 1.1763          | 0.6158   | 0.4306   | 0.6158   |
| 1.1264        | 0.51  | 200  | 1.1235          | 0.622    | 0.4368   | 0.622    |
| 1.1753        | 0.64  | 250  | 1.0747          | 0.6336   | 0.4820   | 0.6336   |
| 1.0741        | 0.77  | 300  | 1.0781          | 0.6326   | 0.4795   | 0.6326   |
| 1.0853        | 0.9   | 350  | 1.0518          | 0.6348   | 0.5281   | 0.6348   |
| 0.9843        | 1.02  | 400  | 1.0083          | 0.6624   | 0.5756   | 0.6624   |
| 0.8793        | 1.15  | 450  | 1.0093          | 0.6602   | 0.5816   | 0.6602   |
| 0.9351        | 1.28  | 500  | 0.9900          | 0.6636   | 0.5725   | 0.6636   |
| 0.9035        | 1.41  | 550  | 0.9779          | 0.6724   | 0.5823   | 0.6724   |
| 0.9223        | 1.53  | 600  | 0.9722          | 0.6742   | 0.5969   | 0.6742   |
| 0.9342        | 1.66  | 650  | 0.9835          | 0.6674   | 0.5931   | 0.6674   |
| 0.8847        | 1.79  | 700  | 0.9589          | 0.6758   | 0.6022   | 0.6758   |
| 0.9263        | 1.92  | 750  | 0.9558          | 0.6736   | 0.6034   | 0.6736   |
| 0.7809        | 2.05  | 800  | 0.9509          | 0.6768   | 0.6071   | 0.6768   |
| 0.8141        | 2.17  | 850  | 0.9482          | 0.6794   | 0.6063   | 0.6794   |
| 0.8932        | 2.3   | 900  | 0.9554          | 0.6764   | 0.6095   | 0.6764   |
| 0.827         | 2.43  | 950  | 0.9510          | 0.6784   | 0.6098   | 0.6784   |
| 0.8278        | 2.56  | 1000 | 0.9565          | 0.6772   | 0.6056   | 0.6772   |
| 0.7278        | 2.69  | 1050 | 0.9521          | 0.6776   | 0.6080   | 0.6776   |
| 0.7698        | 2.81  | 1100 | 0.9474          | 0.6802   | 0.6099   | 0.6802   |
| 0.8179        | 2.94  | 1150 | 0.9466          | 0.6778   | 0.6087   | 0.6778   |


### Framework versions

- Transformers 4.39.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2