File size: 2,360 Bytes
d6c2732 | 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 | ---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: V11-bert-text-classification-model
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. -->
# V11-bert-text-classification-model
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:
- Loss: 0.4161
- Accuracy: 0.8486
- F1: 0.6847
- Precision: 0.7825
- Recall: 0.7045
## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 1.7046 | 0.11 | 50 | 1.7455 | 0.3120 | 0.1170 | 0.2748 | 0.1473 |
| 0.7733 | 0.22 | 100 | 0.6968 | 0.8069 | 0.4921 | 0.4803 | 0.5091 |
| 0.2603 | 0.33 | 150 | 0.5350 | 0.8903 | 0.6622 | 0.6451 | 0.6806 |
| 0.2477 | 0.44 | 200 | 0.4257 | 0.8841 | 0.6558 | 0.6363 | 0.6775 |
| 0.1487 | 0.55 | 250 | 0.3818 | 0.9150 | 0.6781 | 0.6632 | 0.6943 |
| 0.1528 | 0.66 | 300 | 0.3854 | 0.9048 | 0.6753 | 0.6694 | 0.6820 |
| 0.1611 | 0.76 | 350 | 0.2742 | 0.9169 | 0.6783 | 0.8038 | 0.6926 |
| 0.0925 | 0.87 | 400 | 0.2712 | 0.9155 | 0.6796 | 0.6665 | 0.6938 |
| 0.0954 | 0.98 | 450 | 0.3096 | 0.9119 | 0.6948 | 0.7995 | 0.7018 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
|