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# lilt-roberta-DocLayNet-base_lines_ml256-v1

This model is a fine-tuned version of [nielsr/lilt-xlm-roberta-base](https://huggingface.co/nielsr/lilt-xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9004
- Precision: 0.8622
- Recall: 0.8622
- F1: 0.8622
- Accuracy: 0.8622

## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 0.07  | 300   | 0.7371          | 0.6945    | 0.6945 | 0.6945 | 0.6945   |
| 0.7701        | 0.14  | 600   | 0.8573          | 0.7488    | 0.7488 | 0.7488 | 0.7488   |
| 0.7701        | 0.21  | 900   | 0.7687          | 0.7606    | 0.7606 | 0.7606 | 0.7606   |
| 0.471         | 0.27  | 1200  | 0.7057          | 0.7750    | 0.7750 | 0.7750 | 0.7750   |
| 0.4183        | 0.34  | 1500  | 0.6305          | 0.7961    | 0.7961 | 0.7961 | 0.7961   |
| 0.4183        | 0.41  | 1800  | 0.7039          | 0.7769    | 0.7769 | 0.7769 | 0.7769   |
| 0.3683        | 0.48  | 2100  | 0.5956          | 0.7980    | 0.7980 | 0.7980 | 0.7980   |
| 0.3683        | 0.55  | 2400  | 0.7312          | 0.7864    | 0.7864 | 0.7864 | 0.7864   |
| 0.3429        | 0.62  | 2700  | 0.5868          | 0.8049    | 0.8049 | 0.8049 | 0.8049   |
| 0.3337        | 0.69  | 3000  | 0.5911          | 0.8010    | 0.8010 | 0.8010 | 0.8010   |
| 0.3337        | 0.76  | 3300  | 0.7278          | 0.7893    | 0.7893 | 0.7893 | 0.7893   |
| 0.3056        | 0.82  | 3600  | 0.8030          | 0.7908    | 0.7908 | 0.7908 | 0.7908   |
| 0.3056        | 0.89  | 3900  | 0.6587          | 0.7978    | 0.7978 | 0.7978 | 0.7978   |
| 0.2772        | 0.96  | 4200  | 0.5334          | 0.8315    | 0.8315 | 0.8315 | 0.8315   |
| 0.2456        | 1.03  | 4500  | 0.6787          | 0.7992    | 0.7992 | 0.7992 | 0.7992   |
| 0.2456        | 1.1   | 4800  | 0.7325          | 0.8037    | 0.8037 | 0.8037 | 0.8037   |
| 0.2183        | 1.17  | 5100  | 0.7280          | 0.7985    | 0.7985 | 0.7985 | 0.7985   |
| 0.2183        | 1.24  | 5400  | 0.9041          | 0.7787    | 0.7787 | 0.7787 | 0.7787   |
| 0.2288        | 1.31  | 5700  | 0.7504          | 0.8076    | 0.8076 | 0.8076 | 0.8076   |
| 0.2228        | 1.37  | 6000  | 0.6672          | 0.8042    | 0.8042 | 0.8042 | 0.8042   |
| 0.2228        | 1.44  | 6300  | 0.5468          | 0.8511    | 0.8511 | 0.8511 | 0.8511   |
| 0.1989        | 1.51  | 6600  | 0.5928          | 0.8229    | 0.8229 | 0.8229 | 0.8229   |
| 0.1989        | 1.58  | 6900  | 0.6731          | 0.8150    | 0.8150 | 0.8150 | 0.8150   |
| 0.2062        | 1.65  | 7200  | 0.7504          | 0.8030    | 0.8030 | 0.8030 | 0.8030   |
| 0.1971        | 1.72  | 7500  | 0.6554          | 0.8255    | 0.8255 | 0.8255 | 0.8255   |
| 0.1971        | 1.79  | 7800  | 0.7095          | 0.8046    | 0.8046 | 0.8046 | 0.8046   |
| 0.1929        | 1.86  | 8100  | 0.6244          | 0.8397    | 0.8397 | 0.8397 | 0.8397   |
| 0.1929        | 1.92  | 8400  | 0.8521          | 0.8067    | 0.8067 | 0.8067 | 0.8067   |
| 0.1788        | 1.99  | 8700  | 0.7261          | 0.8088    | 0.8088 | 0.8088 | 0.8088   |
| 0.1631        | 2.06  | 9000  | 0.6650          | 0.8272    | 0.8272 | 0.8272 | 0.8272   |
| 0.1631        | 2.13  | 9300  | 0.8314          | 0.8142    | 0.8142 | 0.8142 | 0.8142   |
| 0.1284        | 2.2   | 9600  | 0.9010          | 0.8113    | 0.8113 | 0.8113 | 0.8113   |
| 0.1284        | 2.27  | 9900  | 0.9008          | 0.8087    | 0.8087 | 0.8087 | 0.8087   |
| 0.1248        | 2.34  | 10200 | 0.9152          | 0.8065    | 0.8065 | 0.8065 | 0.8065   |
| 0.1365        | 2.4   | 10500 | 0.6791          | 0.8393    | 0.8393 | 0.8393 | 0.8393   |
| 0.1365        | 2.47  | 10800 | 0.7301          | 0.8185    | 0.8185 | 0.8185 | 0.8185   |
| 0.1194        | 2.54  | 11100 | 0.8937          | 0.8050    | 0.8050 | 0.8050 | 0.8050   |
| 0.1194        | 2.61  | 11400 | 0.7559          | 0.8293    | 0.8293 | 0.8293 | 0.8293   |
| 0.1282        | 2.68  | 11700 | 0.7903          | 0.8163    | 0.8163 | 0.8163 | 0.8163   |
| 0.1234        | 2.75  | 12000 | 1.0103          | 0.8090    | 0.8090 | 0.8090 | 0.8090   |
| 0.1234        | 2.82  | 12300 | 0.9975          | 0.8096    | 0.8096 | 0.8096 | 0.8096   |
| 0.1104        | 2.89  | 12600 | 0.8443          | 0.8171    | 0.8171 | 0.8171 | 0.8171   |
| 0.1104        | 2.95  | 12900 | 0.8380          | 0.8125    | 0.8125 | 0.8125 | 0.8125   |
| 0.1254        | 3.02  | 13200 | 0.8283          | 0.8223    | 0.8223 | 0.8223 | 0.8223   |
| 0.0806        | 3.09  | 13500 | 0.9232          | 0.8323    | 0.8323 | 0.8323 | 0.8323   |
| 0.0806        | 3.16  | 13800 | 1.0903          | 0.8176    | 0.8176 | 0.8176 | 0.8176   |
| 0.0875        | 3.23  | 14100 | 1.0781          | 0.8110    | 0.8110 | 0.8110 | 0.8110   |
| 0.0875        | 3.3   | 14400 | 0.8806          | 0.8277    | 0.8277 | 0.8277 | 0.8277   |
| 0.0817        | 3.37  | 14700 | 1.0024          | 0.8212    | 0.8212 | 0.8212 | 0.8212   |
| 0.085         | 3.44  | 15000 | 0.9078          | 0.8294    | 0.8294 | 0.8294 | 0.8294   |
| 0.085         | 3.5   | 15300 | 0.8745          | 0.8377    | 0.8377 | 0.8377 | 0.8377   |
| 0.0784        | 3.57  | 15600 | 0.9590          | 0.8329    | 0.8329 | 0.8329 | 0.8329   |
| 0.0784        | 3.64  | 15900 | 0.8027          | 0.8500    | 0.8500 | 0.8500 | 0.8500   |
| 0.0785        | 3.71  | 16200 | 1.0033          | 0.8171    | 0.8171 | 0.8171 | 0.8171   |
| 0.0756        | 3.78  | 16500 | 0.8017          | 0.8446    | 0.8446 | 0.8446 | 0.8446   |
| 0.0756        | 3.85  | 16800 | 1.0721          | 0.8162    | 0.8162 | 0.8162 | 0.8162   |
| 0.078         | 3.92  | 17100 | 1.1095          | 0.8172    | 0.8172 | 0.8172 | 0.8172   |
| 0.078         | 3.99  | 17400 | 1.0099          | 0.8200    | 0.8200 | 0.8200 | 0.8200   |
| 0.0696        | 4.05  | 17700 | 1.0189          | 0.8249    | 0.8249 | 0.8249 | 0.8249   |
| 0.0456        | 4.12  | 18000 | 1.2109          | 0.8165    | 0.8165 | 0.8165 | 0.8165   |
| 0.0456        | 4.19  | 18300 | 1.0789          | 0.8273    | 0.8273 | 0.8273 | 0.8273   |
| 0.0587        | 4.26  | 18600 | 1.0981          | 0.8277    | 0.8277 | 0.8277 | 0.8277   |
| 0.0587        | 4.33  | 18900 | 1.0236          | 0.8395    | 0.8395 | 0.8395 | 0.8395   |
| 0.0485        | 4.4   | 19200 | 0.9660          | 0.8381    | 0.8381 | 0.8381 | 0.8381   |
| 0.056         | 4.47  | 19500 | 0.9447          | 0.8453    | 0.8453 | 0.8453 | 0.8453   |
| 0.056         | 4.54  | 19800 | 0.9226          | 0.8564    | 0.8564 | 0.8564 | 0.8564   |
| 0.0517        | 4.6   | 20100 | 1.1416          | 0.8313    | 0.8313 | 0.8313 | 0.8313   |
| 0.0517        | 4.67  | 20400 | 0.9004          | 0.8622    | 0.8622 | 0.8622 | 0.8622   |
| 0.0555        | 4.74  | 20700 | 1.0452          | 0.8416    | 0.8416 | 0.8416 | 0.8416   |
| 0.0578        | 4.81  | 21000 | 0.9997          | 0.8480    | 0.8480 | 0.8480 | 0.8480   |
| 0.0578        | 4.88  | 21300 | 1.0441          | 0.8402    | 0.8402 | 0.8402 | 0.8402   |
| 0.0495        | 4.95  | 21600 | 1.0393          | 0.8421    | 0.8421 | 0.8421 | 0.8421   |


### Framework versions

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

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "nielsr/lilt-xlm-roberta-base", "model-index": [{"name": "lilt-roberta-DocLayNet-base_lines_ml256-v1", "results": []}]}
transformers tensorboard safetensors lilt token-classification generated_from_trainer base_model:nielsr/lilt-xlm-roberta-base license:mit autotrain_compatible endpoints_compatible has_space region:us