---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: athrado/bert-finetuned-nli
  results: []
---

<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->

# athrado/bert-finetuned-nli

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co./bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0529
- Train Accuracy: 0.9836
- Validation Loss: 0.5169
- Validation Accuracy: 0.8646
- Epoch: 4

## 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': False, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 2775, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32

### Training results

| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.5305     | 0.7885         | 0.4253          | 0.8222              | 0     |
| 0.3041     | 0.8887         | 0.3962          | 0.8404              | 1     |
| 0.1947     | 0.9344         | 0.3971          | 0.8444              | 2     |
| 0.0960     | 0.9694         | 0.4772          | 0.8626              | 3     |
| 0.0529     | 0.9836         | 0.5169          | 0.8646              | 4     |


### Framework versions

- Transformers 4.31.0
- TensorFlow 2.13.0
- Datasets 2.14.1
- Tokenizers 0.13.3