---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
base_model: dslim/bert-large-NER
model-index:
- name: bert-finetuned-ner-adam
  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. -->

# bert-finetuned-ner-adam

This model is a fine-tuned version of [dslim/bert-large-NER](https://huggingface.co/dslim/bert-large-NER) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Precision: 0.8340
- Recall: 0.8131
- F1: 0.8234
- Accuracy: 0.9216

## 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: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1744        | 1.0   | 893  | nan             | 0.8276    | 0.8115 | 0.8195 | 0.9205   |
| 0.128         | 2.0   | 1786 | nan             | 0.8404    | 0.8256 | 0.8329 | 0.9238   |
| 0.0768        | 3.0   | 2679 | nan             | 0.8340    | 0.8131 | 0.8234 | 0.9216   |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2