File size: 2,612 Bytes
babce44 f405128 babce44 f405128 babce44 612c171 babce44 f405128 babce44 612c171 babce44 5fd9607 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 |
---
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: ayatsuri/academic-ai-detector
results: []
datasets:
- NicolaiSivesind/human-vs-machine
metrics:
- accuracy
- recall
- precision
- f1
pipeline_tag: text-classification
---
<!-- 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. -->
# ayatsuri/academic-ai-detector
This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on [NicolaiSivesind/human-vs-machine](https://huggingface.co/datasets/NicolaiSivesind/human-vs-machine) dataset.
It achieves the following best results on the evaluation set:
- Train Loss: 0.0910
- Validation Loss: 0.0326
- Train Accuracy: 0.9937
- Train Recall: 0.9927
- Train Precision: 0.9947
- Train F1: 0.9937
- Validation Accuracy: 0.99
- Validation Recall: 0.986
- Validation Precision: 0.9940
- Validation F1: 0.9900
- Epoch: 0
## 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': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2625, '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-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Set | Loss | Accuracy | Recall | Precision | F1 |
|:----------:|:------:|:--------:|:------:|:---------:|:------:|
| Train | 0.0910 | 0.9937 | 0.9927 | 0.9947 | 0.9937 |
| Validation | 0.0326 | 0.99 | 0.986 | 0.9940 | 0.9900 |
### Framework versions
- Transformers 4.41.1
- TensorFlow 2.15.0
- Datasets 2.19.1
- Tokenizers 0.19.1
## Citation
Please use the following citation:
```
@misc {ayatsuri24,
author = { Bagas Nuriksan },
title = { Academic AI Detector },
url = { https://huggingface.co/ayatsuri/academic-ai-detector }
year = 2024,
publisher = { Hugging Face }
}
``` |