---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: checkpoint-10000-finetuned-ner
  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. -->

# checkpoint-10000-finetuned-ner

This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1752
- Precision: 0.7371
- Recall: 0.7711
- F1: 0.7537
- Accuracy: 0.9457

## 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
- distributed_type: tpu
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.4149        | 1.0   | 878  | 0.2236          | 0.6673    | 0.6842 | 0.6757 | 0.9290   |
| 0.1795        | 2.0   | 1756 | 0.1849          | 0.7084    | 0.7581 | 0.7325 | 0.9410   |
| 0.122         | 3.0   | 2634 | 0.1752          | 0.7371    | 0.7711 | 0.7537 | 0.9457   |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1