---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- pv_dataset
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: test_ner3
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: pv_dataset
      type: pv_dataset
      config: PVDatasetCorpus
      split: train
      args: PVDatasetCorpus
    metrics:
    - name: Precision
      type: precision
      value: 0.6698151950718686
    - name: Recall
      type: recall
      value: 0.6499117663801446
    - name: F1
      type: f1
      value: 0.6597133941985438
    - name: Accuracy
      type: accuracy
      value: 0.9606609586670052
---

<!-- 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. -->

# test_ner3

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the pv_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2983
- Precision: 0.6698
- Recall: 0.6499
- F1: 0.6597
- Accuracy: 0.9607

## 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: 20

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1106        | 1.0   | 1813  | 0.1128          | 0.6050    | 0.5949 | 0.5999 | 0.9565   |
| 0.0705        | 2.0   | 3626  | 0.1190          | 0.6279    | 0.6122 | 0.6200 | 0.9585   |
| 0.0433        | 3.0   | 5439  | 0.1458          | 0.6342    | 0.5983 | 0.6157 | 0.9574   |
| 0.0301        | 4.0   | 7252  | 0.1453          | 0.6305    | 0.6818 | 0.6552 | 0.9594   |
| 0.0196        | 5.0   | 9065  | 0.1672          | 0.6358    | 0.6871 | 0.6605 | 0.9594   |
| 0.0133        | 6.0   | 10878 | 0.1931          | 0.6427    | 0.6138 | 0.6279 | 0.9587   |
| 0.0104        | 7.0   | 12691 | 0.1948          | 0.6657    | 0.6511 | 0.6583 | 0.9607   |
| 0.0081        | 8.0   | 14504 | 0.2243          | 0.6341    | 0.6574 | 0.6455 | 0.9586   |
| 0.0054        | 9.0   | 16317 | 0.2432          | 0.6547    | 0.6318 | 0.6431 | 0.9588   |
| 0.0041        | 10.0  | 18130 | 0.2422          | 0.6717    | 0.6397 | 0.6553 | 0.9605   |
| 0.0041        | 11.0  | 19943 | 0.2415          | 0.6571    | 0.6420 | 0.6495 | 0.9601   |
| 0.0027        | 12.0  | 21756 | 0.2567          | 0.6560    | 0.6590 | 0.6575 | 0.9601   |
| 0.0023        | 13.0  | 23569 | 0.2609          | 0.6640    | 0.6495 | 0.6566 | 0.9606   |
| 0.002         | 14.0  | 25382 | 0.2710          | 0.6542    | 0.6670 | 0.6606 | 0.9598   |
| 0.0012        | 15.0  | 27195 | 0.2766          | 0.6692    | 0.6539 | 0.6615 | 0.9610   |
| 0.001         | 16.0  | 29008 | 0.2938          | 0.6692    | 0.6415 | 0.6551 | 0.9603   |
| 0.0007        | 17.0  | 30821 | 0.2969          | 0.6654    | 0.6490 | 0.6571 | 0.9604   |
| 0.0007        | 18.0  | 32634 | 0.3035          | 0.6628    | 0.6456 | 0.6541 | 0.9601   |
| 0.0007        | 19.0  | 34447 | 0.2947          | 0.6730    | 0.6489 | 0.6607 | 0.9609   |
| 0.0004        | 20.0  | 36260 | 0.2983          | 0.6698    | 0.6499 | 0.6597 | 0.9607   |


### Framework versions

- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1