File size: 2,560 Bytes
0f3b231 ca11579 0f3b231 ca11579 0f3b231 ca11579 0f3b231 ca11579 0f3b231 ca11579 0f3b231 ca11579 0f3b231 |
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 85 86 87 88 89 90 91 92 93 94 95 96 |
---
license: mit
tags:
- generated_from_trainer
datasets:
- autextification2023
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: ia-detection-bert-tiny
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: autextification2023
type: autextification2023
config: detection_en
split: train
args: detection_en
metrics:
- name: Accuracy
type: accuracy
value: 0.706714913887871
- name: F1
type: f1
value: 0.7557691574169433
- name: Precision
type: precision
value: 0.6592799627337459
- name: Recall
type: recall
value: 0.8853440571939232
---
<!-- 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. -->
# ia-detection-bert-tiny
This model is a fine-tuned version of [prajjwal1/bert-tiny](https://huggingface.co/prajjwal1/bert-tiny) on the autextification2023 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0749
- Accuracy: 0.7067
- F1: 0.7558
- Precision: 0.6593
- Recall: 0.8853
## 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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.4176 | 1.0 | 3808 | 0.4391 | 0.7962 | 0.7629 | 0.8973 | 0.6635 |
| 0.2567 | 2.0 | 7616 | 0.4912 | 0.8233 | 0.8021 | 0.8984 | 0.7244 |
| 0.2342 | 3.0 | 11424 | 0.5477 | 0.8473 | 0.8355 | 0.8932 | 0.7848 |
| 0.2226 | 4.0 | 15232 | 0.7703 | 0.8059 | 0.7743 | 0.9103 | 0.6736 |
| 0.2706 | 5.0 | 19040 | 0.7108 | 0.8422 | 0.8311 | 0.8825 | 0.7854 |
| 0.1797 | 6.0 | 22848 | 0.8042 | 0.8381 | 0.8314 | 0.8567 | 0.8075 |
### Framework versions
- Transformers 4.26.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.13.3
|