---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-ner_0220_J_ORIDATA
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# distilbert-base-uncased-finetuned-ner_0220_J_ORIDATA

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: 0.4949
- Precision: 0.8987
- Recall: 0.9551
- F1: 0.9260
- Accuracy: 0.9437

## 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   | 111  | 0.2996          | 0.7246    | 0.8407 | 0.7783 | 0.9295   |
| No log        | 2.0   | 222  | 0.2545          | 0.8067    | 0.8983 | 0.8500 | 0.9343   |
| No log        | 3.0   | 333  | 0.2398          | 0.8649    | 0.9280 | 0.8953 | 0.9445   |
| No log        | 4.0   | 444  | 0.2326          | 0.8651    | 0.9297 | 0.8962 | 0.9459   |
| 0.329         | 5.0   | 555  | 0.2200          | 0.8836    | 0.9263 | 0.9044 | 0.9459   |
| 0.329         | 6.0   | 666  | 0.2382          | 0.8365    | 0.9280 | 0.8799 | 0.9315   |
| 0.329         | 7.0   | 777  | 0.2349          | 0.8775    | 0.9347 | 0.9052 | 0.9395   |
| 0.329         | 8.0   | 888  | 0.2604          | 0.8832    | 0.9424 | 0.9118 | 0.9415   |
| 0.329         | 9.0   | 999  | 0.2705          | 0.8929    | 0.9331 | 0.9126 | 0.9483   |
| 0.124         | 10.0  | 1110 | 0.2986          | 0.8731    | 0.9390 | 0.9049 | 0.9410   |
| 0.124         | 11.0  | 1221 | 0.2960          | 0.8805    | 0.9432 | 0.9108 | 0.9441   |
| 0.124         | 12.0  | 1332 | 0.2798          | 0.8778    | 0.9373 | 0.9066 | 0.9401   |
| 0.124         | 13.0  | 1443 | 0.3364          | 0.8851    | 0.9534 | 0.9180 | 0.9422   |
| 0.0735        | 14.0  | 1554 | 0.3546          | 0.8943    | 0.9534 | 0.9229 | 0.9425   |
| 0.0735        | 15.0  | 1665 | 0.3449          | 0.8917    | 0.9492 | 0.9195 | 0.9443   |
| 0.0735        | 16.0  | 1776 | 0.3789          | 0.8817    | 0.9407 | 0.9102 | 0.9381   |
| 0.0735        | 17.0  | 1887 | 0.3727          | 0.8803    | 0.9407 | 0.9095 | 0.9443   |
| 0.0735        | 18.0  | 1998 | 0.3633          | 0.8841    | 0.9373 | 0.9099 | 0.9387   |
| 0.0453        | 19.0  | 2109 | 0.4279          | 0.8816    | 0.9398 | 0.9098 | 0.9328   |
| 0.0453        | 20.0  | 2220 | 0.4055          | 0.8939    | 0.95   | 0.9211 | 0.9431   |
| 0.0453        | 21.0  | 2331 | 0.4138          | 0.8924    | 0.9492 | 0.9199 | 0.9441   |
| 0.0453        | 22.0  | 2442 | 0.4275          | 0.8948    | 0.9441 | 0.9188 | 0.9422   |
| 0.0283        | 23.0  | 2553 | 0.4319          | 0.8955    | 0.9508 | 0.9223 | 0.9418   |
| 0.0283        | 24.0  | 2664 | 0.4179          | 0.8878    | 0.9525 | 0.9191 | 0.9464   |
| 0.0283        | 25.0  | 2775 | 0.4341          | 0.8937    | 0.9407 | 0.9166 | 0.9420   |
| 0.0283        | 26.0  | 2886 | 0.4420          | 0.9063    | 0.9508 | 0.9280 | 0.9439   |
| 0.0283        | 27.0  | 2997 | 0.4370          | 0.8986    | 0.9466 | 0.9220 | 0.9432   |
| 0.0188        | 28.0  | 3108 | 0.4557          | 0.8953    | 0.9492 | 0.9214 | 0.9430   |
| 0.0188        | 29.0  | 3219 | 0.4458          | 0.8934    | 0.9449 | 0.9185 | 0.9437   |
| 0.0188        | 30.0  | 3330 | 0.4461          | 0.8973    | 0.9475 | 0.9217 | 0.9425   |
| 0.0188        | 31.0  | 3441 | 0.4638          | 0.9014    | 0.9525 | 0.9262 | 0.9426   |
| 0.0132        | 32.0  | 3552 | 0.4732          | 0.9029    | 0.9534 | 0.9275 | 0.9437   |
| 0.0132        | 33.0  | 3663 | 0.4645          | 0.9062    | 0.9576 | 0.9312 | 0.9453   |
| 0.0132        | 34.0  | 3774 | 0.4542          | 0.8981    | 0.9483 | 0.9225 | 0.9447   |
| 0.0132        | 35.0  | 3885 | 0.4702          | 0.8974    | 0.9492 | 0.9226 | 0.9431   |
| 0.0132        | 36.0  | 3996 | 0.4824          | 0.9081    | 0.9542 | 0.9306 | 0.9428   |
| 0.0101        | 37.0  | 4107 | 0.4757          | 0.8978    | 0.9534 | 0.9248 | 0.9442   |
| 0.0101        | 38.0  | 4218 | 0.4750          | 0.8971    | 0.9534 | 0.9244 | 0.9453   |
| 0.0101        | 39.0  | 4329 | 0.4843          | 0.9008    | 0.9542 | 0.9267 | 0.9446   |
| 0.0101        | 40.0  | 4440 | 0.4840          | 0.9019    | 0.9585 | 0.9293 | 0.9464   |
| 0.0077        | 41.0  | 4551 | 0.4852          | 0.8939    | 0.9492 | 0.9207 | 0.9436   |
| 0.0077        | 42.0  | 4662 | 0.4864          | 0.9051    | 0.9542 | 0.9290 | 0.9447   |
| 0.0077        | 43.0  | 4773 | 0.4801          | 0.9010    | 0.9559 | 0.9276 | 0.9431   |
| 0.0077        | 44.0  | 4884 | 0.4887          | 0.9016    | 0.9551 | 0.9276 | 0.9435   |
| 0.0077        | 45.0  | 4995 | 0.4973          | 0.8972    | 0.9542 | 0.9248 | 0.9430   |
| 0.0065        | 46.0  | 5106 | 0.4942          | 0.9       | 0.9534 | 0.9259 | 0.9436   |
| 0.0065        | 47.0  | 5217 | 0.4933          | 0.9007    | 0.9534 | 0.9263 | 0.9436   |
| 0.0065        | 48.0  | 5328 | 0.4979          | 0.8987    | 0.9551 | 0.9260 | 0.9434   |
| 0.0065        | 49.0  | 5439 | 0.4966          | 0.9009    | 0.9551 | 0.9272 | 0.9434   |
| 0.0059        | 50.0  | 5550 | 0.4949          | 0.8987    | 0.9551 | 0.9260 | 0.9437   |


### Framework versions

- Transformers 4.20.1
- Pytorch 1.13.0+cu117
- Datasets 2.8.0
- Tokenizers 0.12.1