|
--- |
|
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: 0.6591 |
|
- Accuracy: 0.62 |
|
- F1: 0.6205 |
|
|
|
## 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 | 0.01 | 50 | 0.6891 | 0.54 | 0.3787 | |
|
| No log | 0.01 | 100 | 0.6893 | 0.54 | 0.3787 | |
|
| No log | 0.02 | 150 | 0.6876 | 0.54 | 0.3787 | |
|
| No log | 0.03 | 200 | 0.6978 | 0.48 | 0.4231 | |
|
| No log | 0.03 | 250 | 0.6899 | 0.5 | 0.4878 | |
|
| No log | 0.04 | 300 | 0.6825 | 0.57 | 0.5577 | |
|
| No log | 0.04 | 350 | 0.6782 | 0.62 | 0.6205 | |
|
| No log | 0.05 | 400 | 0.6692 | 0.6 | 0.5981 | |
|
| No log | 0.06 | 450 | 0.6688 | 0.58 | 0.5664 | |
|
| 0.6773 | 0.06 | 500 | 0.6692 | 0.6 | 0.5966 | |
|
| 0.6773 | 0.07 | 550 | 0.6642 | 0.62 | 0.62 | |
|
| 0.6773 | 0.08 | 600 | 0.6577 | 0.65 | 0.6505 | |
|
| 0.6773 | 0.08 | 650 | 0.6618 | 0.6 | 0.5992 | |
|
| 0.6773 | 0.09 | 700 | 0.6617 | 0.62 | 0.62 | |
|
| 0.6773 | 0.09 | 750 | 0.6641 | 0.62 | 0.6205 | |
|
| 0.6773 | 0.1 | 800 | 0.6573 | 0.62 | 0.62 | |
|
| 0.6773 | 0.11 | 850 | 0.6625 | 0.61 | 0.6096 | |
|
| 0.6773 | 0.11 | 900 | 0.6625 | 0.63 | 0.6303 | |
|
| 0.6773 | 0.12 | 950 | 0.6632 | 0.62 | 0.6181 | |
|
| 0.6414 | 0.13 | 1000 | 0.6613 | 0.62 | 0.6206 | |
|
| 0.6414 | 0.13 | 1050 | 0.6594 | 0.62 | 0.6206 | |
|
| 0.6414 | 0.14 | 1100 | 0.6607 | 0.62 | 0.6206 | |
|
| 0.6414 | 0.14 | 1150 | 0.6580 | 0.62 | 0.6205 | |
|
| 0.6414 | 0.15 | 1200 | 0.6628 | 0.62 | 0.6205 | |
|
| 0.6414 | 0.16 | 1250 | 0.6591 | 0.62 | 0.6205 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.35.2 |
|
- Pytorch 2.1.0+cu118 |
|
- Datasets 2.15.0 |
|
- Tokenizers 0.15.0 |
|
|
