---
license: apache-2.0
base_model: bert-large-uncased
tags:
- generated_from_trainer
datasets:
- wnut_17
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: sample_data
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: wnut_17
      type: wnut_17
      config: wnut_17
      split: validation
      args: wnut_17
    metrics:
    - name: Precision
      type: precision
      value: 0.7290715372907154
    - name: Recall
      type: recall
      value: 0.5729665071770335
    - name: F1
      type: f1
      value: 0.6416610850636303
    - name: Accuracy
      type: accuracy
      value: 0.9602644796236252
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# sample_data

This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on the wnut_17 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2684
- Precision: 0.7291
- Recall: 0.5730
- F1: 0.6417
- Accuracy: 0.9603

## 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.6403        | 0.12  | 25   | 0.4914          | 0.0       | 0.0    | 0.0    | 0.9205   |
| 0.334         | 0.23  | 50   | 0.4539          | 0.0       | 0.0    | 0.0    | 0.9205   |
| 0.2346        | 0.35  | 75   | 0.3556          | 0.4118    | 0.0419 | 0.0760 | 0.9236   |
| 0.2352        | 0.47  | 100  | 0.2936          | 0.4337    | 0.2464 | 0.3143 | 0.9341   |
| 0.1725        | 0.59  | 125  | 0.2898          | 0.4983    | 0.3421 | 0.4057 | 0.9372   |
| 0.1449        | 0.7   | 150  | 0.2858          | 0.4606    | 0.3493 | 0.3973 | 0.9399   |
| 0.1548        | 0.82  | 175  | 0.2487          | 0.5699    | 0.3900 | 0.4631 | 0.9435   |
| 0.1429        | 0.94  | 200  | 0.3071          | 0.6888    | 0.3469 | 0.4614 | 0.9415   |
| 0.1506        | 1.06  | 225  | 0.2252          | 0.4820    | 0.4952 | 0.4885 | 0.9465   |
| 0.1196        | 1.17  | 250  | 0.2512          | 0.5463    | 0.4940 | 0.5188 | 0.9485   |
| 0.1062        | 1.29  | 275  | 0.2916          | 0.6395    | 0.4605 | 0.5355 | 0.9495   |
| 0.0983        | 1.41  | 300  | 0.2402          | 0.6199    | 0.5443 | 0.5796 | 0.9497   |
| 0.1068        | 1.53  | 325  | 0.2470          | 0.6018    | 0.4773 | 0.5324 | 0.9504   |
| 0.0879        | 1.64  | 350  | 0.2360          | 0.6468    | 0.5586 | 0.5995 | 0.9511   |
| 0.0928        | 1.76  | 375  | 0.2267          | 0.6126    | 0.5467 | 0.5777 | 0.9514   |
| 0.1045        | 1.88  | 400  | 0.2258          | 0.6934    | 0.5060 | 0.5851 | 0.9542   |
| 0.0933        | 2.0   | 425  | 0.2403          | 0.6954    | 0.5108 | 0.5890 | 0.9547   |
| 0.0497        | 2.11  | 450  | 0.2539          | 0.6460    | 0.5371 | 0.5865 | 0.9554   |
| 0.0607        | 2.23  | 475  | 0.3065          | 0.7293    | 0.4737 | 0.5743 | 0.9523   |
| 0.0857        | 2.35  | 500  | 0.2565          | 0.6770    | 0.4964 | 0.5728 | 0.9545   |
| 0.0513        | 2.46  | 525  | 0.2569          | 0.6931    | 0.5323 | 0.6022 | 0.9569   |
| 0.0697        | 2.58  | 550  | 0.2273          | 0.7193    | 0.5670 | 0.6341 | 0.9566   |
| 0.0446        | 2.7   | 575  | 0.2361          | 0.6348    | 0.5634 | 0.5970 | 0.9580   |
| 0.0498        | 2.82  | 600  | 0.2544          | 0.7109    | 0.5323 | 0.6088 | 0.9579   |
| 0.0464        | 2.93  | 625  | 0.2576          | 0.7237    | 0.5514 | 0.6259 | 0.9589   |
| 0.0441        | 3.05  | 650  | 0.2691          | 0.7321    | 0.5490 | 0.6275 | 0.9586   |
| 0.0524        | 3.17  | 675  | 0.2368          | 0.6947    | 0.5825 | 0.6337 | 0.9603   |
| 0.0335        | 3.29  | 700  | 0.2488          | 0.6991    | 0.5670 | 0.6262 | 0.9594   |
| 0.0349        | 3.4   | 725  | 0.2564          | 0.7084    | 0.5347 | 0.6094 | 0.9580   |
| 0.026         | 3.52  | 750  | 0.2523          | 0.7085    | 0.5610 | 0.6262 | 0.9594   |
| 0.0314        | 3.64  | 775  | 0.2647          | 0.7335    | 0.5467 | 0.6265 | 0.9584   |
| 0.0213        | 3.76  | 800  | 0.2551          | 0.7032    | 0.5754 | 0.6329 | 0.9603   |
| 0.0312        | 3.87  | 825  | 0.2470          | 0.7034    | 0.5957 | 0.6451 | 0.9606   |
| 0.0313        | 3.99  | 850  | 0.2693          | 0.7421    | 0.5610 | 0.6390 | 0.9598   |
| 0.0243        | 4.11  | 875  | 0.2699          | 0.7345    | 0.5658 | 0.6392 | 0.9598   |
| 0.0289        | 4.23  | 900  | 0.2535          | 0.7143    | 0.5682 | 0.6329 | 0.9603   |
| 0.0226        | 4.34  | 925  | 0.2581          | 0.7205    | 0.5706 | 0.6368 | 0.9602   |
| 0.0173        | 4.46  | 950  | 0.2644          | 0.7145    | 0.5718 | 0.6352 | 0.9601   |
| 0.0139        | 4.58  | 975  | 0.2705          | 0.7164    | 0.5682 | 0.6338 | 0.9600   |
| 0.0243        | 4.69  | 1000 | 0.2615          | 0.7116    | 0.5813 | 0.6399 | 0.9606   |
| 0.0222        | 4.81  | 1025 | 0.2642          | 0.7229    | 0.5742 | 0.64   | 0.9606   |
| 0.0112        | 4.93  | 1050 | 0.2684          | 0.7291    | 0.5730 | 0.6417 | 0.9603   |


### Framework versions

- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3