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# ner_scibert_pretrained

This model is a fine-tuned version of [AmedeoBonatti/mlm_scibert_uncased](https://huggingface.co/AmedeoBonatti/mlm_scibert_uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5034
- Precision: 0.7311
- Recall: 0.7845
- F1: 0.7568
- Accuracy: 0.9212

## 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: 50
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 28   | 0.4780          | 0.5021    | 0.5068 | 0.5045 | 0.8511   |
| No log        | 2.0   | 56   | 0.2756          | 0.6755    | 0.7187 | 0.6965 | 0.9078   |
| No log        | 3.0   | 84   | 0.2488          | 0.6943    | 0.7593 | 0.7253 | 0.9145   |
| No log        | 4.0   | 112  | 0.2429          | 0.7014    | 0.7817 | 0.7394 | 0.9172   |
| No log        | 5.0   | 140  | 0.2481          | 0.7093    | 0.7743 | 0.7404 | 0.9197   |
| No log        | 6.0   | 168  | 0.2505          | 0.7243    | 0.7901 | 0.7558 | 0.9213   |
| No log        | 7.0   | 196  | 0.2618          | 0.7228    | 0.7874 | 0.7537 | 0.9204   |
| No log        | 8.0   | 224  | 0.2704          | 0.7302    | 0.7924 | 0.76   | 0.9233   |
| No log        | 9.0   | 252  | 0.2874          | 0.7350    | 0.7869 | 0.7601 | 0.9220   |
| No log        | 10.0  | 280  | 0.2984          | 0.7298    | 0.7804 | 0.7542 | 0.9218   |
| No log        | 11.0  | 308  | 0.3142          | 0.7292    | 0.7829 | 0.7551 | 0.9216   |
| No log        | 12.0  | 336  | 0.3303          | 0.7224    | 0.7856 | 0.7527 | 0.9196   |
| No log        | 13.0  | 364  | 0.3403          | 0.7257    | 0.7897 | 0.7563 | 0.9206   |
| No log        | 14.0  | 392  | 0.3551          | 0.7355    | 0.7779 | 0.7561 | 0.9206   |
| No log        | 15.0  | 420  | 0.3662          | 0.7306    | 0.7813 | 0.7551 | 0.9213   |
| No log        | 16.0  | 448  | 0.3782          | 0.7355    | 0.7833 | 0.7586 | 0.9218   |
| No log        | 17.0  | 476  | 0.3847          | 0.7439    | 0.7790 | 0.7611 | 0.9212   |
| 0.1475        | 18.0  | 504  | 0.3961          | 0.7270    | 0.7962 | 0.7601 | 0.9210   |
| 0.1475        | 19.0  | 532  | 0.4030          | 0.7296    | 0.7845 | 0.7560 | 0.9213   |
| 0.1475        | 20.0  | 560  | 0.4153          | 0.7368    | 0.7817 | 0.7586 | 0.9219   |
| 0.1475        | 21.0  | 588  | 0.4171          | 0.7315    | 0.7906 | 0.7599 | 0.9217   |
| 0.1475        | 22.0  | 616  | 0.4212          | 0.7344    | 0.7840 | 0.7584 | 0.9225   |
| 0.1475        | 23.0  | 644  | 0.4350          | 0.7353    | 0.7858 | 0.7597 | 0.9215   |
| 0.1475        | 24.0  | 672  | 0.4459          | 0.7371    | 0.7854 | 0.7605 | 0.9218   |
| 0.1475        | 25.0  | 700  | 0.4472          | 0.7322    | 0.7897 | 0.7599 | 0.9214   |
| 0.1475        | 26.0  | 728  | 0.4512          | 0.7338    | 0.7933 | 0.7624 | 0.9218   |
| 0.1475        | 27.0  | 756  | 0.4588          | 0.7363    | 0.7833 | 0.7591 | 0.9215   |
| 0.1475        | 28.0  | 784  | 0.4589          | 0.7255    | 0.7926 | 0.7576 | 0.9217   |
| 0.1475        | 29.0  | 812  | 0.4580          | 0.7316    | 0.7854 | 0.7575 | 0.9206   |
| 0.1475        | 30.0  | 840  | 0.4611          | 0.7319    | 0.7840 | 0.7571 | 0.9205   |
| 0.1475        | 31.0  | 868  | 0.4662          | 0.7335    | 0.7854 | 0.7585 | 0.9216   |
| 0.1475        | 32.0  | 896  | 0.4731          | 0.7331    | 0.7874 | 0.7593 | 0.9215   |
| 0.1475        | 33.0  | 924  | 0.4799          | 0.7349    | 0.7849 | 0.7591 | 0.9220   |
| 0.1475        | 34.0  | 952  | 0.4816          | 0.7336    | 0.7820 | 0.7570 | 0.9208   |
| 0.1475        | 35.0  | 980  | 0.4836          | 0.7311    | 0.7863 | 0.7577 | 0.9209   |
| 0.0075        | 36.0  | 1008 | 0.4886          | 0.7318    | 0.7831 | 0.7566 | 0.9210   |
| 0.0075        | 37.0  | 1036 | 0.4905          | 0.7352    | 0.7865 | 0.7600 | 0.9212   |
| 0.0075        | 38.0  | 1064 | 0.4931          | 0.7302    | 0.7863 | 0.7572 | 0.9210   |
| 0.0075        | 39.0  | 1092 | 0.4933          | 0.7332    | 0.7856 | 0.7585 | 0.9215   |
| 0.0075        | 40.0  | 1120 | 0.4954          | 0.7320    | 0.7876 | 0.7588 | 0.9214   |
| 0.0075        | 41.0  | 1148 | 0.4948          | 0.7351    | 0.7876 | 0.7605 | 0.9222   |
| 0.0075        | 42.0  | 1176 | 0.4993          | 0.7322    | 0.7858 | 0.7581 | 0.9212   |
| 0.0075        | 43.0  | 1204 | 0.4994          | 0.7320    | 0.7833 | 0.7568 | 0.9210   |
| 0.0075        | 44.0  | 1232 | 0.4993          | 0.7347    | 0.7858 | 0.7594 | 0.9214   |
| 0.0075        | 45.0  | 1260 | 0.5010          | 0.7322    | 0.7865 | 0.7584 | 0.9217   |
| 0.0075        | 46.0  | 1288 | 0.5002          | 0.7314    | 0.7856 | 0.7575 | 0.9213   |
| 0.0075        | 47.0  | 1316 | 0.5016          | 0.7323    | 0.7863 | 0.7583 | 0.9217   |
| 0.0075        | 48.0  | 1344 | 0.5023          | 0.7321    | 0.7847 | 0.7575 | 0.9211   |
| 0.0075        | 49.0  | 1372 | 0.5030          | 0.7316    | 0.7845 | 0.7571 | 0.9212   |
| 0.0075        | 50.0  | 1400 | 0.5034          | 0.7311    | 0.7845 | 0.7568 | 0.9212   |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.2.0
- Datasets 2.16.1
- Tokenizers 0.15.1

{"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "AmedeoBonatti/mlm_scibert_uncased", "model-index": [{"name": "ner_scibert_pretrained", "results": []}]}
transformers safetensors bert token-classification generated_from_trainer base_model:AmedeoBonatti/mlm_scibert_uncased autotrain_compatible endpoints_compatible region:us