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# Merged-MM-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: 0.5525
- Accuracy: 0.7777
- F1: 0.8749

## 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: 3e-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: 7

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log        | 0.0   | 50   | 0.6929          | 0.526    | 0.3813 |
| No log        | 0.0   | 100  | 0.6938          | 0.48     | 0.3125 |
| No log        | 0.01  | 150  | 0.6971          | 0.479    | 0.3103 |
| No log        | 0.01  | 200  | 0.6948          | 0.479    | 0.3103 |
| No log        | 0.01  | 250  | 0.6938          | 0.479    | 0.3103 |
| No log        | 0.01  | 300  | 0.6939          | 0.479    | 0.3103 |
| No log        | 0.01  | 350  | 0.6927          | 0.521    | 0.3587 |
| No log        | 0.02  | 400  | 0.6931          | 0.501    | 0.4988 |
| No log        | 0.02  | 450  | 0.6944          | 0.479    | 0.3103 |
| 0.6942        | 0.02  | 500  | 0.6954          | 0.479    | 0.3103 |
| 0.6942        | 0.02  | 550  | 0.6960          | 0.479    | 0.3103 |
| 0.6942        | 0.02  | 600  | 0.6934          | 0.486    | 0.3322 |
| 0.6942        | 0.02  | 650  | 0.6970          | 0.479    | 0.3103 |
| 0.6942        | 0.03  | 700  | 0.6929          | 0.535    | 0.4767 |
| 0.6942        | 0.03  | 750  | 0.6931          | 0.499    | 0.4609 |
| 0.6942        | 0.03  | 800  | 0.6952          | 0.479    | 0.3103 |
| 0.6942        | 0.03  | 850  | 0.6933          | 0.48     | 0.3160 |
| 0.6942        | 0.03  | 900  | 0.6979          | 0.479    | 0.3103 |
| 0.6942        | 0.04  | 950  | 0.6940          | 0.479    | 0.3103 |
| 0.6938        | 0.04  | 1000 | 0.6915          | 0.521    | 0.3569 |
| 0.6938        | 0.04  | 1050 | 0.6942          | 0.479    | 0.3103 |
| 0.6938        | 0.04  | 1100 | 0.6884          | 0.519    | 0.3630 |
| 0.6938        | 0.04  | 1150 | 0.6849          | 0.596    | 0.5817 |
| 0.6938        | 0.05  | 1200 | 0.6849          | 0.547    | 0.5131 |
| 0.6938        | 0.05  | 1250 | 0.6771          | 0.568    | 0.5502 |
| 0.6938        | 0.05  | 1300 | 0.6792          | 0.572    | 0.5558 |
| 0.6938        | 0.05  | 1350 | 0.6889          | 0.55     | 0.5161 |
| 0.6938        | 0.05  | 1400 | 0.6792          | 0.59     | 0.5828 |
| 0.6938        | 0.06  | 1450 | 0.6729          | 0.602    | 0.5987 |
| 0.6781        | 0.06  | 1500 | 0.6702          | 0.592    | 0.5822 |
| 0.6781        | 0.06  | 1550 | 0.6711          | 0.578    | 0.5633 |
| 0.6781        | 0.06  | 1600 | 0.6642          | 0.607    | 0.6024 |
| 0.6781        | 0.06  | 1650 | 0.6624          | 0.592    | 0.5819 |
| 0.6781        | 0.07  | 1700 | 0.6585          | 0.595    | 0.5883 |
| 0.6781        | 0.07  | 1750 | 0.6543          | 0.584    | 0.5740 |
| 0.6781        | 0.07  | 1800 | 0.6452          | 0.6      | 0.5926 |
| 0.6781        | 0.07  | 1850 | 0.6355          | 0.615    | 0.6106 |
| 0.6781        | 0.07  | 1900 | 0.6280          | 0.615    | 0.6090 |
| 0.6781        | 0.07  | 1950 | 0.6209          | 0.621    | 0.6139 |
| 0.6465        | 0.08  | 2000 | 0.6178          | 0.632    | 0.6247 |
| 0.6465        | 0.08  | 2050 | 0.6133          | 0.641    | 0.6303 |
| 0.6465        | 0.08  | 2100 | 0.6132          | 0.629    | 0.6218 |
| 0.6465        | 0.08  | 2150 | 0.6155          | 0.63     | 0.6289 |
| 0.6465        | 0.08  | 2200 | 0.5984          | 0.635    | 0.6322 |
| 0.6465        | 0.09  | 2250 | 0.6065          | 0.633    | 0.6102 |
| 0.6465        | 0.09  | 2300 | 0.5968          | 0.629    | 0.6063 |
| 0.6465        | 0.09  | 2350 | 0.5871          | 0.649    | 0.6411 |
| 0.6465        | 0.09  | 2400 | 0.5824          | 0.64     | 0.6218 |
| 0.6465        | 0.09  | 2450 | 0.5812          | 0.643    | 0.6390 |
| 0.6042        | 0.1   | 2500 | 0.5790          | 0.644    | 0.6355 |
| 0.6042        | 0.1   | 2550 | 0.5744          | 0.654    | 0.6507 |
| 0.6042        | 0.1   | 2600 | 0.5679          | 0.641    | 0.6292 |
| 0.6042        | 0.1   | 2650 | 0.5707          | 0.644    | 0.6311 |
| 0.6042        | 0.1   | 2700 | 0.5707          | 0.652    | 0.6439 |
| 0.6042        | 0.11  | 2750 | 0.5680          | 0.661    | 0.6569 |
| 0.6042        | 0.11  | 2800 | 0.5592          | 0.67     | 0.6684 |
| 0.6042        | 0.11  | 2850 | 0.5557          | 0.678    | 0.6758 |
| 0.6042        | 0.11  | 2900 | 0.5579          | 0.671    | 0.6690 |
| 0.6042        | 0.11  | 2950 | 0.5490          | 0.692    | 0.6909 |
| 0.5834        | 0.11  | 3000 | 0.5474          | 0.688    | 0.6858 |
| 0.5834        | 0.12  | 3050 | 0.5447          | 0.696    | 0.6902 |
| 0.5834        | 0.12  | 3100 | 0.5456          | 0.699    | 0.6985 |
| 0.5834        | 0.12  | 3150 | 0.5592          | 0.675    | 0.6628 |
| 0.5834        | 0.12  | 3200 | 0.5442          | 0.69     | 0.6856 |
| 0.5834        | 0.12  | 3250 | 0.5424          | 0.698    | 0.6974 |
| 0.5834        | 0.13  | 3300 | 0.5464          | 0.691    | 0.6907 |
| 0.5834        | 0.13  | 3350 | 0.5433          | 0.693    | 0.6922 |
| 0.5834        | 0.13  | 3400 | 0.5400          | 0.746    | 0.7461 |
| 0.5834        | 0.13  | 3450 | 0.5406          | 0.712    | 0.7091 |
| 0.5551        | 0.13  | 3500 | 0.5367          | 0.738    | 0.7376 |
| 0.5551        | 0.14  | 3550 | 0.5354          | 0.713    | 0.7091 |
| 0.5551        | 0.14  | 3600 | 0.5377          | 0.74     | 0.7400 |
| 0.5551        | 0.14  | 3650 | 0.5342          | 0.751    | 0.7506 |
| 0.5551        | 0.14  | 3700 | 0.5386          | 0.701    | 0.6992 |
| 0.5551        | 0.14  | 3750 | 0.5395          | 0.737    | 0.7368 |
| 0.5551        | 0.15  | 3800 | 0.5333          | 0.733    | 0.7330 |
| 0.5551        | 0.15  | 3850 | 0.5245          | 0.737    | 0.7371 |
| 0.5551        | 0.15  | 3900 | 0.5236          | 0.745    | 0.7451 |
| 0.5551        | 0.15  | 3950 | 0.5149          | 0.741    | 0.7400 |
| 0.5508        | 0.15  | 4000 | 0.5208          | 0.743    | 0.7422 |
| 0.5508        | 0.16  | 4050 | 0.5109          | 0.744    | 0.7440 |
| 0.5508        | 0.16  | 4100 | 0.5179          | 0.742    | 0.7398 |
| 0.5508        | 0.16  | 4150 | 0.5133          | 0.75     | 0.7499 |
| 0.5508        | 0.16  | 4200 | 0.5110          | 0.744    | 0.7416 |
| 0.5508        | 0.16  | 4250 | 0.5133          | 0.749    | 0.7476 |
| 0.5508        | 0.16  | 4300 | 0.5075          | 0.743    | 0.7410 |
| 0.5508        | 0.17  | 4350 | 0.5108          | 0.755    | 0.7544 |
| 0.5508        | 0.17  | 4400 | 0.5051          | 0.747    | 0.7465 |
| 0.5508        | 0.17  | 4450 | 0.5064          | 0.746    | 0.7455 |
| 0.5362        | 0.17  | 4500 | 0.5030          | 0.744    | 0.7441 |
| 0.5362        | 0.17  | 4550 | 0.5043          | 0.748    | 0.7476 |
| 0.5362        | 0.18  | 4600 | 0.5010          | 0.753    | 0.7531 |
| 0.5362        | 0.18  | 4650 | 0.4988          | 0.762    | 0.7616 |
| 0.5362        | 0.18  | 4700 | 0.4999          | 0.755    | 0.7548 |
| 0.5362        | 0.18  | 4750 | 0.5159          | 0.754    | 0.7529 |
| 0.5362        | 0.18  | 4800 | 0.4924          | 0.764    | 0.7639 |
| 0.5362        | 0.19  | 4850 | 0.4935          | 0.755    | 0.7549 |
| 0.5362        | 0.19  | 4900 | 0.4874          | 0.76     | 0.7601 |
| 0.5362        | 0.19  | 4950 | 0.4859          | 0.759    | 0.7591 |
| 0.5226        | 0.19  | 5000 | 0.4901          | 0.761    | 0.7610 |
| 0.5226        | 0.19  | 5050 | 0.4740          | 0.779    | 0.7790 |
| 0.5226        | 0.2   | 5100 | 0.4799          | 0.783    | 0.7831 |
| 0.5226        | 0.2   | 5150 | 0.4833          | 0.771    | 0.7698 |
| 0.5226        | 0.2   | 5200 | 0.4879          | 0.759    | 0.7561 |
| 0.5226        | 0.2   | 5250 | 0.4812          | 0.772    | 0.7719 |
| 0.5226        | 0.2   | 5300 | 0.4825          | 0.772    | 0.7715 |
| 0.5226        | 0.2   | 5350 | 0.4791          | 0.775    | 0.7744 |
| 0.5226        | 0.21  | 5400 | 0.4749          | 0.773    | 0.7729 |
| 0.5226        | 0.21  | 5450 | 0.4691          | 0.782    | 0.7811 |
| 0.5055        | 0.21  | 5500 | 0.4752          | 0.78     | 0.7791 |
| 0.5055        | 0.21  | 5550 | 0.4621          | 0.766    | 0.7645 |
| 0.5055        | 0.21  | 5600 | 0.4628          | 0.779    | 0.7790 |
| 0.5055        | 0.22  | 5650 | 0.4543          | 0.776    | 0.7760 |
| 0.5055        | 0.22  | 5700 | 0.4548          | 0.786    | 0.7861 |
| 0.5055        | 0.22  | 5750 | 0.4578          | 0.777    | 0.7763 |
| 0.5055        | 0.22  | 5800 | 0.4684          | 0.778    | 0.7780 |
| 0.5055        | 0.22  | 5850 | 0.4626          | 0.775    | 0.7751 |
| 0.5055        | 0.23  | 5900 | 0.4714          | 0.785    | 0.7850 |
| 0.5055        | 0.23  | 5950 | 0.4514          | 0.79     | 0.7896 |
| 0.4985        | 0.23  | 6000 | 0.4541          | 0.773    | 0.7731 |
| 0.4985        | 0.23  | 6050 | 0.4587          | 0.788    | 0.7876 |
| 0.4985        | 0.23  | 6100 | 0.4523          | 0.787    | 0.7867 |
| 0.4985        | 0.24  | 6150 | 0.4441          | 0.787    | 0.7870 |
| 0.4985        | 0.24  | 6200 | 0.4529          | 0.784    | 0.7841 |
| 0.4985        | 0.24  | 6250 | 0.4512          | 0.784    | 0.7840 |
| 0.4985        | 0.24  | 6300 | 0.4545          | 0.777    | 0.7757 |
| 0.4985        | 0.24  | 6350 | 0.4399          | 0.788    | 0.7874 |
| 0.4985        | 0.25  | 6400 | 0.4478          | 0.794    | 0.7939 |
| 0.4985        | 0.25  | 6450 | 0.4495          | 0.793    | 0.7930 |
| 0.4937        | 0.25  | 6500 | 0.4454          | 0.792    | 0.7913 |
| 0.4937        | 0.25  | 6550 | 0.4438          | 0.795    | 0.7950 |
| 0.4937        | 0.25  | 6600 | 0.4476          | 0.795    | 0.7948 |
| 0.4937        | 0.25  | 6650 | 0.4448          | 0.794    | 0.7939 |
| 0.4937        | 0.26  | 6700 | 0.4472          | 0.791    | 0.7911 |
| 0.4937        | 0.26  | 6750 | 0.4431          | 0.793    | 0.7924 |
| 0.4937        | 0.26  | 6800 | 0.4434          | 0.796    | 0.7958 |
| 0.4937        | 0.26  | 6850 | 0.4340          | 0.802    | 0.802  |
| 0.4937        | 0.26  | 6900 | 0.4502          | 0.786    | 0.7848 |
| 0.4937        | 0.27  | 6950 | 0.4349          | 0.797    | 0.7964 |
| 0.4826        | 0.27  | 7000 | 0.4348          | 0.79     | 0.7894 |
| 0.4826        | 0.27  | 7050 | 0.4321          | 0.788    | 0.7875 |
| 0.4826        | 0.27  | 7100 | 0.4300          | 0.787    | 0.7868 |
| 0.4826        | 0.27  | 7150 | 0.4346          | 0.78     | 0.7779 |
| 0.4826        | 0.28  | 7200 | 0.4246          | 0.802    | 0.8020 |
| 0.4826        | 0.28  | 7250 | 0.4273          | 0.793    | 0.7930 |
| 0.4826        | 0.28  | 7300 | 0.4346          | 0.79     | 0.7894 |
| 0.4826        | 0.28  | 7350 | 0.4358          | 0.789    | 0.7887 |
| 0.4826        | 0.28  | 7400 | 0.4368          | 0.788    | 0.7871 |
| 0.4826        | 0.29  | 7450 | 0.4426          | 0.784    | 0.7841 |
| 0.4756        | 0.29  | 7500 | 0.4312          | 0.802    | 0.8019 |
| 0.4756        | 0.29  | 7550 | 0.4303          | 0.795    | 0.7944 |
| 0.4756        | 0.29  | 7600 | 0.4391          | 0.792    | 0.7916 |
| 0.4756        | 0.29  | 7650 | 0.4325          | 0.793    | 0.7922 |
| 0.4756        | 0.29  | 7700 | 0.4283          | 0.793    | 0.7920 |
| 0.4756        | 0.3   | 7750 | 0.4271          | 0.799    | 0.7991 |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "base_model": "prajjwal1/bert-tiny", "model-index": [{"name": "Merged-MM-praj", "results": []}]}
transformers tensorboard safetensors bert text-classification generated_from_trainer base_model:prajjwal1/bert-tiny license:mit autotrain_compatible endpoints_compatible region:us