---
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