---
license: mit
base_model: dslim/bert-large-NER
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Adam-NER-Model
  results: []
datasets:
- conll2003
- rungalileo/mit_movies
- hyperhustle/ner-dataset
language:
- en
pipeline_tag: token-classification
---

<!-- 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 [hyperhustle/ner-dataset](https://huggingface.co/datasets/hyperhustle/ner-dataset) dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Precision: 0.8845
- Recall: 0.8749
- F1: 0.8797
- Accuracy: 0.9646

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0949        | 1.0   | 3080 | nan             | 0.8914    | 0.8942 | 0.8928 | 0.9663   |
| 0.0574        | 2.0   | 6160 | nan             | 0.8763    | 0.8784 | 0.8773 | 0.9635   |
| 0.0376        | 3.0   | 9240 | nan             | 0.8845    | 0.8749 | 0.8797 | 0.9646   |


### Framework versions

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