---
license: apache-2.0
base_model: om-ashish-soni/pos-ner-tagging-v2
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: pos-ner-tagging-v2
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: conll2003
      type: conll2003
      config: conll2003
      split: validation
      args: conll2003
    metrics:
    - name: Precision
      type: precision
      value: 0.9393653920267203
    - name: Recall
      type: recall
      value: 0.9408358887483113
    - name: F1
      type: f1
      value: 0.9401000653531749
    - name: Accuracy
      type: accuracy
      value: 0.9270324365691411
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# pos-ner-tagging-v2

This model is a fine-tuned version of [om-ashish-soni/pos-ner-tagging-v2](https://huggingface.co/om-ashish-soni/pos-ner-tagging-v2) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6442
- Precision: 0.9394
- Recall: 0.9408
- F1: 0.9401
- Accuracy: 0.9270

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 16

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3297        | 1.0   | 1756  | 0.4190          | 0.9189    | 0.9231 | 0.9210 | 0.9051   |
| 0.2521        | 2.0   | 3512  | 0.3836          | 0.9210    | 0.9300 | 0.9255 | 0.9114   |
| 0.1932        | 3.0   | 5268  | 0.4155          | 0.9295    | 0.9338 | 0.9316 | 0.9183   |
| 0.1325        | 4.0   | 7024  | 0.3969          | 0.9328    | 0.9356 | 0.9342 | 0.9211   |
| 0.0973        | 5.0   | 8780  | 0.4247          | 0.9332    | 0.9367 | 0.9349 | 0.9222   |
| 0.0799        | 6.0   | 10536 | 0.4606          | 0.9338    | 0.9374 | 0.9356 | 0.9229   |
| 0.0554        | 7.0   | 12292 | 0.4836          | 0.9333    | 0.9379 | 0.9356 | 0.9239   |
| 0.0415        | 8.0   | 14048 | 0.5271          | 0.9361    | 0.9391 | 0.9376 | 0.9245   |
| 0.0285        | 9.0   | 15804 | 0.5363          | 0.9366    | 0.9397 | 0.9381 | 0.9253   |
| 0.022         | 10.0  | 17560 | 0.5653          | 0.9377    | 0.9396 | 0.9387 | 0.9258   |
| 0.0146        | 11.0  | 19316 | 0.5962          | 0.9374    | 0.9400 | 0.9387 | 0.9259   |
| 0.0121        | 12.0  | 21072 | 0.6061          | 0.9385    | 0.9401 | 0.9393 | 0.9266   |
| 0.0085        | 13.0  | 22828 | 0.6263          | 0.9384    | 0.9403 | 0.9394 | 0.9261   |
| 0.0062        | 14.0  | 24584 | 0.6365          | 0.9381    | 0.9399 | 0.9390 | 0.9259   |
| 0.0053        | 15.0  | 26340 | 0.6386          | 0.9384    | 0.9402 | 0.9393 | 0.9264   |
| 0.0042        | 16.0  | 28096 | 0.6442          | 0.9394    | 0.9408 | 0.9401 | 0.9270   |


### Framework versions

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