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license: mit
base_model: prajjwal1/bert-tiny
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: Merged-Int-praj
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. -->
# Merged-Int-praj
This model is a fine-tuned version of [prajjwal1/bert-tiny](https://huggingface.co/prajjwal1/bert-tiny) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1460
- Accuracy: 0.96
- F1: 0.9600
## 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 | 0.6933 | 0.5 | 0.3333 |
| No log | 0.01 | 100 | 0.6929 | 0.58 | 0.4900 |
| No log | 0.01 | 150 | 0.6937 | 0.5 | 0.3333 |
| No log | 0.01 | 200 | 0.6951 | 0.5 | 0.3333 |
| No log | 0.02 | 250 | 0.6902 | 0.52 | 0.5130 |
| No log | 0.02 | 300 | 0.6909 | 0.5 | 0.3333 |
| No log | 0.02 | 350 | 0.6795 | 0.56 | 0.4762 |
| No log | 0.03 | 400 | 0.6524 | 0.61 | 0.6010 |
| No log | 0.03 | 450 | 0.6139 | 0.71 | 0.7100 |
| 0.6779 | 0.03 | 500 | 0.5827 | 0.71 | 0.7033 |
| 0.6779 | 0.04 | 550 | 0.5732 | 0.71 | 0.7033 |
| 0.6779 | 0.04 | 600 | 0.5467 | 0.74 | 0.7396 |
| 0.6779 | 0.04 | 650 | 0.5174 | 0.8 | 0.7980 |
| 0.6779 | 0.05 | 700 | 0.5193 | 0.74 | 0.7399 |
| 0.6779 | 0.05 | 750 | 0.4905 | 0.8 | 0.7980 |
| 0.6779 | 0.05 | 800 | 0.4710 | 0.8 | 0.7980 |
| 0.6779 | 0.06 | 850 | 0.4523 | 0.83 | 0.8271 |
| 0.6779 | 0.06 | 900 | 0.4373 | 0.84 | 0.8368 |
| 0.6779 | 0.06 | 950 | 0.4214 | 0.84 | 0.8368 |
| 0.5615 | 0.07 | 1000 | 0.4086 | 0.84 | 0.8368 |
| 0.5615 | 0.07 | 1050 | 0.3803 | 0.84 | 0.8368 |
| 0.5615 | 0.07 | 1100 | 0.3476 | 0.9 | 0.8994 |
| 0.5615 | 0.08 | 1150 | 0.3218 | 0.91 | 0.9096 |
| 0.5615 | 0.08 | 1200 | 0.3028 | 0.91 | 0.9096 |
| 0.5615 | 0.08 | 1250 | 0.2851 | 0.92 | 0.9195 |
| 0.5615 | 0.09 | 1300 | 0.2737 | 0.92 | 0.9195 |
| 0.5615 | 0.09 | 1350 | 0.2637 | 0.91 | 0.9096 |
| 0.5615 | 0.09 | 1400 | 0.2560 | 0.92 | 0.9195 |
| 0.5615 | 0.1 | 1450 | 0.2426 | 0.92 | 0.9199 |
| 0.4267 | 0.1 | 1500 | 0.2390 | 0.89 | 0.8897 |
| 0.4267 | 0.1 | 1550 | 0.2320 | 0.92 | 0.9199 |
| 0.4267 | 0.11 | 1600 | 0.2239 | 0.93 | 0.9298 |
| 0.4267 | 0.11 | 1650 | 0.2159 | 0.94 | 0.9398 |
| 0.4267 | 0.11 | 1700 | 0.2156 | 0.93 | 0.9298 |
| 0.4267 | 0.12 | 1750 | 0.2079 | 0.93 | 0.9298 |
| 0.4267 | 0.12 | 1800 | 0.1938 | 0.93 | 0.9298 |
| 0.4267 | 0.12 | 1850 | 0.1909 | 0.93 | 0.9298 |
| 0.4267 | 0.13 | 1900 | 0.1923 | 0.93 | 0.9298 |
| 0.4267 | 0.13 | 1950 | 0.1893 | 0.94 | 0.9398 |
| 0.3491 | 0.13 | 2000 | 0.1633 | 0.96 | 0.9600 |
| 0.3491 | 0.14 | 2050 | 0.1662 | 0.95 | 0.9500 |
| 0.3491 | 0.14 | 2100 | 0.1494 | 0.96 | 0.9600 |
| 0.3491 | 0.14 | 2150 | 0.1606 | 0.95 | 0.9499 |
| 0.3491 | 0.15 | 2200 | 0.1595 | 0.96 | 0.9599 |
| 0.3491 | 0.15 | 2250 | 0.1460 | 0.96 | 0.9600 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0
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