---
license: mit
library_name: peft
tags:
- generated_from_trainer
metrics:
- accuracy
base_model: dslim/bert-base-NER
model-index:
- name: STS-Lora-Fine-Tuning-Capstone-bert-testing-23-with-lower-r-mid
  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. -->

# STS-Lora-Fine-Tuning-Capstone-bert-testing-23-with-lower-r-mid

This model is a fine-tuned version of [dslim/bert-base-NER](https://huggingface.co/dslim/bert-base-NER) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3610
- Accuracy: 0.4300

## 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: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 40

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 180  | 1.7491          | 0.2429   |
| No log        | 2.0   | 360  | 1.7395          | 0.2451   |
| 1.7055        | 3.0   | 540  | 1.7242          | 0.2451   |
| 1.7055        | 4.0   | 720  | 1.6937          | 0.2980   |
| 1.7055        | 5.0   | 900  | 1.6446          | 0.3038   |
| 1.6419        | 6.0   | 1080 | 1.6173          | 0.3176   |
| 1.6419        | 7.0   | 1260 | 1.5638          | 0.3401   |
| 1.6419        | 8.0   | 1440 | 1.5355          | 0.3524   |
| 1.5258        | 9.0   | 1620 | 1.5112          | 0.3590   |
| 1.5258        | 10.0  | 1800 | 1.4870          | 0.3742   |
| 1.5258        | 11.0  | 1980 | 1.4729          | 0.3749   |
| 1.4424        | 12.0  | 2160 | 1.4664          | 0.3938   |
| 1.4424        | 13.0  | 2340 | 1.4524          | 0.4003   |
| 1.4002        | 14.0  | 2520 | 1.4390          | 0.4061   |
| 1.4002        | 15.0  | 2700 | 1.4317          | 0.4090   |
| 1.4002        | 16.0  | 2880 | 1.4241          | 0.4155   |
| 1.376         | 17.0  | 3060 | 1.4201          | 0.4148   |
| 1.376         | 18.0  | 3240 | 1.4069          | 0.4083   |
| 1.376         | 19.0  | 3420 | 1.4000          | 0.4184   |
| 1.3533        | 20.0  | 3600 | 1.3978          | 0.4235   |
| 1.3533        | 21.0  | 3780 | 1.3929          | 0.4329   |
| 1.3533        | 22.0  | 3960 | 1.3896          | 0.4329   |
| 1.3336        | 23.0  | 4140 | 1.3856          | 0.4264   |
| 1.3336        | 24.0  | 4320 | 1.3833          | 0.4322   |
| 1.3254        | 25.0  | 4500 | 1.3787          | 0.4235   |
| 1.3254        | 26.0  | 4680 | 1.3744          | 0.4329   |
| 1.3254        | 27.0  | 4860 | 1.3751          | 0.4300   |
| 1.3082        | 28.0  | 5040 | 1.3720          | 0.4336   |
| 1.3082        | 29.0  | 5220 | 1.3687          | 0.4300   |
| 1.3082        | 30.0  | 5400 | 1.3674          | 0.4293   |
| 1.3105        | 31.0  | 5580 | 1.3663          | 0.4373   |
| 1.3105        | 32.0  | 5760 | 1.3643          | 0.4351   |
| 1.3105        | 33.0  | 5940 | 1.3630          | 0.4271   |
| 1.295         | 34.0  | 6120 | 1.3628          | 0.4322   |
| 1.295         | 35.0  | 6300 | 1.3625          | 0.4300   |
| 1.295         | 36.0  | 6480 | 1.3623          | 0.4307   |
| 1.2919        | 37.0  | 6660 | 1.3617          | 0.4322   |
| 1.2919        | 38.0  | 6840 | 1.3613          | 0.4315   |
| 1.2905        | 39.0  | 7020 | 1.3610          | 0.4300   |
| 1.2905        | 40.0  | 7200 | 1.3610          | 0.4300   |


### Framework versions

- PEFT 0.10.0
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2