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# distilbert-base-uncased-finetuned-ner

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4331
- Precision: 0.7801
- Recall: 0.7129
- F1: 0.745
- Accuracy: 0.9363

## 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

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 23   | 0.8283          | 0.0       | 0.0    | 0.0    | 0.8332   |
| No log        | 2.0   | 46   | 0.5781          | 0.4474    | 0.0813 | 0.1377 | 0.8482   |
| No log        | 3.0   | 69   | 0.4927          | 0.4841    | 0.3636 | 0.4153 | 0.8913   |
| No log        | 4.0   | 92   | 0.4348          | 0.4657    | 0.4545 | 0.4600 | 0.9044   |
| No log        | 5.0   | 115  | 0.4293          | 0.5561    | 0.4976 | 0.5253 | 0.9088   |
| No log        | 6.0   | 138  | 0.3934          | 0.6313    | 0.5981 | 0.6143 | 0.9244   |
| No log        | 7.0   | 161  | 0.3961          | 0.7219    | 0.6459 | 0.6818 | 0.9313   |
| No log        | 8.0   | 184  | 0.3648          | 0.7098    | 0.6555 | 0.6816 | 0.9325   |
| No log        | 9.0   | 207  | 0.3961          | 0.7582    | 0.6603 | 0.7059 | 0.9357   |
| No log        | 10.0  | 230  | 0.3800          | 0.7474    | 0.6794 | 0.7118 | 0.9350   |
| No log        | 11.0  | 253  | 0.3661          | 0.7474    | 0.6794 | 0.7118 | 0.9332   |
| No log        | 12.0  | 276  | 0.3697          | 0.7619    | 0.6890 | 0.7236 | 0.9344   |
| No log        | 13.0  | 299  | 0.3829          | 0.7660    | 0.6890 | 0.7254 | 0.9350   |
| No log        | 14.0  | 322  | 0.3859          | 0.7849    | 0.6986 | 0.7392 | 0.9350   |
| No log        | 15.0  | 345  | 0.3760          | 0.7946    | 0.7033 | 0.7462 | 0.9375   |
| No log        | 16.0  | 368  | 0.3609          | 0.7602    | 0.7129 | 0.7358 | 0.9357   |
| No log        | 17.0  | 391  | 0.3687          | 0.7766    | 0.6986 | 0.7355 | 0.9350   |
| No log        | 18.0  | 414  | 0.3856          | 0.8043    | 0.7081 | 0.7532 | 0.9375   |
| No log        | 19.0  | 437  | 0.3901          | 0.7861    | 0.7033 | 0.7424 | 0.9369   |
| No log        | 20.0  | 460  | 0.4151          | 0.8276    | 0.6890 | 0.7520 | 0.9388   |
| No log        | 21.0  | 483  | 0.3892          | 0.7824    | 0.7225 | 0.7512 | 0.9382   |
| 0.1775        | 22.0  | 506  | 0.3952          | 0.7947    | 0.7225 | 0.7569 | 0.9375   |
| 0.1775        | 23.0  | 529  | 0.3906          | 0.7817    | 0.7368 | 0.7586 | 0.9382   |
| 0.1775        | 24.0  | 552  | 0.4132          | 0.8156    | 0.6986 | 0.7526 | 0.9413   |
| 0.1775        | 25.0  | 575  | 0.4048          | 0.7979    | 0.7177 | 0.7557 | 0.9388   |
| 0.1775        | 26.0  | 598  | 0.4026          | 0.7772    | 0.7177 | 0.7463 | 0.9363   |
| 0.1775        | 27.0  | 621  | 0.4084          | 0.7789    | 0.7081 | 0.7419 | 0.9363   |
| 0.1775        | 28.0  | 644  | 0.4081          | 0.7865    | 0.7225 | 0.7531 | 0.9375   |
| 0.1775        | 29.0  | 667  | 0.4058          | 0.7795    | 0.7273 | 0.7525 | 0.9375   |
| 0.1775        | 30.0  | 690  | 0.4100          | 0.7772    | 0.7177 | 0.7463 | 0.9369   |
| 0.1775        | 31.0  | 713  | 0.4146          | 0.7824    | 0.7225 | 0.7512 | 0.9363   |
| 0.1775        | 32.0  | 736  | 0.4142          | 0.7865    | 0.7225 | 0.7531 | 0.9363   |
| 0.1775        | 33.0  | 759  | 0.4168          | 0.7824    | 0.7225 | 0.7512 | 0.9369   |
| 0.1775        | 34.0  | 782  | 0.4367          | 0.8122    | 0.7033 | 0.7538 | 0.9388   |
| 0.1775        | 35.0  | 805  | 0.4282          | 0.7884    | 0.7129 | 0.7487 | 0.9363   |
| 0.1775        | 36.0  | 828  | 0.4249          | 0.7842    | 0.7129 | 0.7469 | 0.9357   |
| 0.1775        | 37.0  | 851  | 0.4297          | 0.7884    | 0.7129 | 0.7487 | 0.9363   |
| 0.1775        | 38.0  | 874  | 0.4218          | 0.7824    | 0.7225 | 0.7512 | 0.9375   |
| 0.1775        | 39.0  | 897  | 0.4267          | 0.7842    | 0.7129 | 0.7469 | 0.9363   |
| 0.1775        | 40.0  | 920  | 0.4272          | 0.7937    | 0.7177 | 0.7538 | 0.9369   |
| 0.1775        | 41.0  | 943  | 0.4308          | 0.7926    | 0.7129 | 0.7506 | 0.9363   |
| 0.1775        | 42.0  | 966  | 0.4390          | 0.7884    | 0.7129 | 0.7487 | 0.9363   |
| 0.1775        | 43.0  | 989  | 0.4366          | 0.7914    | 0.7081 | 0.7475 | 0.9375   |
| 0.0065        | 44.0  | 1012 | 0.4311          | 0.7749    | 0.7081 | 0.74   | 0.9350   |
| 0.0065        | 45.0  | 1035 | 0.4276          | 0.7760    | 0.7129 | 0.7431 | 0.9357   |
| 0.0065        | 46.0  | 1058 | 0.4313          | 0.7801    | 0.7129 | 0.745  | 0.9357   |
| 0.0065        | 47.0  | 1081 | 0.4330          | 0.7801    | 0.7129 | 0.745  | 0.9357   |
| 0.0065        | 48.0  | 1104 | 0.4325          | 0.7801    | 0.7129 | 0.745  | 0.9363   |
| 0.0065        | 49.0  | 1127 | 0.4328          | 0.7801    | 0.7129 | 0.745  | 0.9363   |
| 0.0065        | 50.0  | 1150 | 0.4331          | 0.7801    | 0.7129 | 0.745  | 0.9363   |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.0
- Tokenizers 0.15.0

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