File size: 3,095 Bytes
389a4bf d02beb9 389a4bf d02beb9 389a4bf |
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 |
---
license: mit
base_model: prajjwal1/bert-tiny
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: TestForColab
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. -->
# TestForColab
This model is a fine-tuned version of [prajjwal1/bert-tiny](https://huggingface.co/prajjwal1/bert-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6515
- Accuracy: 0.56
- F1: 0.5579
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 0.0 | 50 | 0.6897 | 0.54 | 0.3787 |
| No log | 0.01 | 100 | 0.6899 | 0.6 | 0.5926 |
| No log | 0.01 | 150 | 0.6952 | 0.46 | 0.2899 |
| No log | 0.01 | 200 | 0.6874 | 0.63 | 0.6194 |
| No log | 0.02 | 250 | 0.6849 | 0.64 | 0.6092 |
| No log | 0.02 | 300 | 0.6929 | 0.46 | 0.2899 |
| No log | 0.02 | 350 | 0.6830 | 0.6 | 0.5390 |
| No log | 0.03 | 400 | 0.6821 | 0.54 | 0.3787 |
| No log | 0.03 | 450 | 0.6812 | 0.63 | 0.6095 |
| 0.6924 | 0.03 | 500 | 0.6806 | 0.62 | 0.6077 |
| 0.6924 | 0.04 | 550 | 0.6770 | 0.62 | 0.5969 |
| 0.6924 | 0.04 | 600 | 0.6805 | 0.58 | 0.5746 |
| 0.6924 | 0.04 | 650 | 0.6800 | 0.59 | 0.5857 |
| 0.6924 | 0.05 | 700 | 0.6732 | 0.63 | 0.6008 |
| 0.6924 | 0.05 | 750 | 0.6820 | 0.56 | 0.5387 |
| 0.6924 | 0.05 | 800 | 0.6652 | 0.64 | 0.6253 |
| 0.6924 | 0.06 | 850 | 0.6634 | 0.59 | 0.5896 |
| 0.6924 | 0.06 | 900 | 0.6604 | 0.61 | 0.6103 |
| 0.6924 | 0.06 | 950 | 0.6733 | 0.62 | 0.5936 |
| 0.6842 | 0.07 | 1000 | 0.6590 | 0.65 | 0.6176 |
| 0.6842 | 0.07 | 1050 | 0.6549 | 0.6 | 0.6005 |
| 0.6842 | 0.07 | 1100 | 0.6521 | 0.63 | 0.6242 |
| 0.6842 | 0.08 | 1150 | 0.6524 | 0.61 | 0.6015 |
| 0.6842 | 0.08 | 1200 | 0.6587 | 0.57 | 0.5634 |
| 0.6842 | 0.09 | 1250 | 0.6515 | 0.56 | 0.5579 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|