---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner-ime
  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-ime

This model is a fine-tuned version of [bert-base-cased](https://huggingface.co./bert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4592
- Precision: 0.6456
- Recall: 0.3813
- F1: 0.4794
- Accuracy: 0.6108

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 221  | 2.6044          | 0.6263    | 0.3774 | 0.4710 | 0.6081   |
| No log        | 2.0   | 442  | 2.5040          | 0.6286    | 0.3848 | 0.4774 | 0.6100   |
| 2.7612        | 3.0   | 663  | 2.4592          | 0.6456    | 0.3813 | 0.4794 | 0.6108   |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.11.0