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# results

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0750
- Accuracy: 0.6202
- F1: 0.6107

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log        | 0.02  | 20   | 2.2641          | 0.2781   | 0.1211 |
| No log        | 0.04  | 40   | 2.0495          | 0.2786   | 0.1221 |
| No log        | 0.05  | 60   | 1.8930          | 0.4343   | 0.3708 |
| No log        | 0.07  | 80   | 1.8073          | 0.4046   | 0.3218 |
| No log        | 0.09  | 100  | 1.7021          | 0.4710   | 0.4005 |
| No log        | 0.11  | 120  | 1.6779          | 0.4715   | 0.4077 |
| No log        | 0.12  | 140  | 1.6050          | 0.4804   | 0.4139 |
| No log        | 0.14  | 160  | 1.5298          | 0.5032   | 0.4416 |
| No log        | 0.16  | 180  | 1.4980          | 0.5037   | 0.4427 |
| No log        | 0.18  | 200  | 1.4566          | 0.5112   | 0.4553 |
| No log        | 0.19  | 220  | 1.4303          | 0.5295   | 0.4736 |
| No log        | 0.21  | 240  | 1.4033          | 0.5340   | 0.4835 |
| No log        | 0.23  | 260  | 1.4549          | 0.5052   | 0.4588 |
| No log        | 0.25  | 280  | 1.3637          | 0.5404   | 0.5032 |
| No log        | 0.26  | 300  | 1.3285          | 0.5473   | 0.5067 |
| No log        | 0.28  | 320  | 1.3536          | 0.5454   | 0.5199 |
| No log        | 0.3   | 340  | 1.3069          | 0.5528   | 0.5166 |
| No log        | 0.32  | 360  | 1.2906          | 0.5464   | 0.5134 |
| No log        | 0.33  | 380  | 1.2867          | 0.5612   | 0.5355 |
| No log        | 0.35  | 400  | 1.2854          | 0.5493   | 0.5145 |
| No log        | 0.37  | 420  | 1.2707          | 0.5702   | 0.5465 |
| No log        | 0.39  | 440  | 1.2698          | 0.5667   | 0.5484 |
| No log        | 0.41  | 460  | 1.2531          | 0.5766   | 0.5637 |
| No log        | 0.42  | 480  | 1.2348          | 0.5801   | 0.5592 |
| No log        | 0.44  | 500  | 1.2392          | 0.5756   | 0.5531 |
| No log        | 0.46  | 520  | 1.2110          | 0.5821   | 0.5584 |
| No log        | 0.48  | 540  | 1.2050          | 0.5771   | 0.5546 |
| No log        | 0.49  | 560  | 1.2078          | 0.5756   | 0.5482 |
| No log        | 0.51  | 580  | 1.2088          | 0.5776   | 0.5518 |
| No log        | 0.53  | 600  | 1.1987          | 0.5905   | 0.5758 |
| No log        | 0.55  | 620  | 1.1893          | 0.5761   | 0.5548 |
| No log        | 0.56  | 640  | 1.1991          | 0.5801   | 0.5667 |
| No log        | 0.58  | 660  | 1.1939          | 0.5865   | 0.5755 |
| No log        | 0.6   | 680  | 1.1701          | 0.5875   | 0.5653 |
| No log        | 0.62  | 700  | 1.1852          | 0.5870   | 0.5709 |
| No log        | 0.63  | 720  | 1.1699          | 0.5845   | 0.5668 |
| No log        | 0.65  | 740  | 1.1778          | 0.5910   | 0.5731 |
| No log        | 0.67  | 760  | 1.1546          | 0.5984   | 0.5806 |
| No log        | 0.69  | 780  | 1.1720          | 0.5890   | 0.5687 |
| No log        | 0.7   | 800  | 1.1554          | 0.5940   | 0.5730 |
| No log        | 0.72  | 820  | 1.1454          | 0.5910   | 0.5748 |
| No log        | 0.74  | 840  | 1.1457          | 0.6034   | 0.5893 |
| No log        | 0.76  | 860  | 1.1631          | 0.5935   | 0.5663 |
| No log        | 0.78  | 880  | 1.1277          | 0.6059   | 0.5930 |
| No log        | 0.79  | 900  | 1.1291          | 0.5984   | 0.5839 |
| No log        | 0.81  | 920  | 1.1345          | 0.5954   | 0.5826 |
| No log        | 0.83  | 940  | 1.1253          | 0.6034   | 0.5868 |
| No log        | 0.85  | 960  | 1.1397          | 0.5989   | 0.5899 |
| No log        | 0.86  | 980  | 1.1223          | 0.6009   | 0.5890 |
| No log        | 0.88  | 1000 | 1.1363          | 0.6059   | 0.5917 |
| No log        | 0.9   | 1020 | 1.1391          | 0.5944   | 0.5787 |
| No log        | 0.92  | 1040 | 1.1480          | 0.5860   | 0.5732 |
| No log        | 0.93  | 1060 | 1.1065          | 0.6063   | 0.5925 |
| No log        | 0.95  | 1080 | 1.1250          | 0.5984   | 0.5844 |
| No log        | 0.97  | 1100 | 1.1344          | 0.5935   | 0.5835 |
| No log        | 0.99  | 1120 | 1.1452          | 0.5880   | 0.5700 |
| 1.3609        | 1.0   | 1140 | 1.1084          | 0.6093   | 0.5960 |
| 1.3609        | 1.02  | 1160 | 1.1079          | 0.6098   | 0.6006 |
| 1.3609        | 1.04  | 1180 | 1.1178          | 0.6073   | 0.5951 |
| 1.3609        | 1.06  | 1200 | 1.1267          | 0.6004   | 0.5878 |
| 1.3609        | 1.07  | 1220 | 1.1084          | 0.6098   | 0.5977 |
| 1.3609        | 1.09  | 1240 | 1.1109          | 0.6039   | 0.5910 |
| 1.3609        | 1.11  | 1260 | 1.1142          | 0.6078   | 0.5968 |
| 1.3609        | 1.13  | 1280 | 1.1218          | 0.6024   | 0.5915 |
| 1.3609        | 1.15  | 1300 | 1.0929          | 0.6197   | 0.6057 |
| 1.3609        | 1.16  | 1320 | 1.1001          | 0.6108   | 0.5995 |
| 1.3609        | 1.18  | 1340 | 1.1049          | 0.6068   | 0.5993 |
| 1.3609        | 1.2   | 1360 | 1.1019          | 0.6098   | 0.5971 |
| 1.3609        | 1.22  | 1380 | 1.1140          | 0.6029   | 0.5942 |
| 1.3609        | 1.23  | 1400 | 1.0981          | 0.6078   | 0.5991 |
| 1.3609        | 1.25  | 1420 | 1.1129          | 0.6078   | 0.5965 |
| 1.3609        | 1.27  | 1440 | 1.0896          | 0.6173   | 0.6076 |
| 1.3609        | 1.29  | 1460 | 1.1062          | 0.6059   | 0.5935 |
| 1.3609        | 1.3   | 1480 | 1.1038          | 0.6143   | 0.5996 |
| 1.3609        | 1.32  | 1500 | 1.0906          | 0.6182   | 0.6072 |
| 1.3609        | 1.34  | 1520 | 1.0883          | 0.6182   | 0.6099 |
| 1.3609        | 1.36  | 1540 | 1.1114          | 0.6024   | 0.5955 |
| 1.3609        | 1.37  | 1560 | 1.0884          | 0.6158   | 0.6044 |
| 1.3609        | 1.39  | 1580 | 1.0994          | 0.6088   | 0.6021 |
| 1.3609        | 1.41  | 1600 | 1.0857          | 0.6187   | 0.6055 |
| 1.3609        | 1.43  | 1620 | 1.1067          | 0.6098   | 0.6027 |
| 1.3609        | 1.44  | 1640 | 1.0845          | 0.6237   | 0.6167 |
| 1.3609        | 1.46  | 1660 | 1.0899          | 0.6177   | 0.6065 |
| 1.3609        | 1.48  | 1680 | 1.0925          | 0.6177   | 0.6067 |
| 1.3609        | 1.5   | 1700 | 1.0831          | 0.6148   | 0.6042 |
| 1.3609        | 1.52  | 1720 | 1.0988          | 0.6108   | 0.6017 |
| 1.3609        | 1.53  | 1740 | 1.0810          | 0.6173   | 0.6069 |
| 1.3609        | 1.55  | 1760 | 1.0857          | 0.6163   | 0.6049 |
| 1.3609        | 1.57  | 1780 | 1.1003          | 0.6103   | 0.5985 |
| 1.3609        | 1.59  | 1800 | 1.0861          | 0.6143   | 0.6045 |
| 1.3609        | 1.6   | 1820 | 1.1024          | 0.6073   | 0.5976 |
| 1.3609        | 1.62  | 1840 | 1.0786          | 0.6222   | 0.6094 |
| 1.3609        | 1.64  | 1860 | 1.0984          | 0.6088   | 0.5968 |
| 1.3609        | 1.66  | 1880 | 1.0750          | 0.6227   | 0.6146 |
| 1.3609        | 1.67  | 1900 | 1.0849          | 0.6108   | 0.6029 |
| 1.3609        | 1.69  | 1920 | 1.0787          | 0.6192   | 0.6102 |
| 1.3609        | 1.71  | 1940 | 1.0831          | 0.6133   | 0.6036 |
| 1.3609        | 1.73  | 1960 | 1.0739          | 0.6197   | 0.6136 |
| 1.3609        | 1.74  | 1980 | 1.0766          | 0.6187   | 0.6092 |
| 1.3609        | 1.76  | 2000 | 1.0778          | 0.6207   | 0.6119 |
| 1.3609        | 1.78  | 2020 | 1.0739          | 0.6168   | 0.6053 |
| 1.3609        | 1.8   | 2040 | 1.0736          | 0.6182   | 0.6066 |
| 1.3609        | 1.81  | 2060 | 1.0723          | 0.6212   | 0.6107 |
| 1.3609        | 1.83  | 2080 | 1.0792          | 0.6222   | 0.6126 |
| 1.3609        | 1.85  | 2100 | 1.0823          | 0.6177   | 0.6091 |
| 1.3609        | 1.87  | 2120 | 1.0706          | 0.6197   | 0.6094 |
| 1.3609        | 1.89  | 2140 | 1.0729          | 0.6217   | 0.6107 |
| 1.3609        | 1.9   | 2160 | 1.0728          | 0.6202   | 0.6094 |
| 1.3609        | 1.92  | 2180 | 1.0754          | 0.6187   | 0.6083 |
| 1.3609        | 1.94  | 2200 | 1.0775          | 0.6192   | 0.6094 |
| 1.3609        | 1.96  | 2220 | 1.0773          | 0.6187   | 0.6090 |
| 1.3609        | 1.97  | 2240 | 1.0767          | 0.6177   | 0.6082 |
| 1.3609        | 1.99  | 2260 | 1.0750          | 0.6202   | 0.6107 |


### Framework versions

- Transformers 4.36.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.15.0

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "results", "results": []}]}
transformers safetensors distilbert text-classification generated_from_trainer base_model:distilbert-base-uncased license:apache-2.0 autotrain_compatible endpoints_compatible region:us