---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
metrics:
- accuracy
base_model: rv2307/electra-small-ner
model-index:
- name: STS-Lora-Fine-Tuning-Capstone-electra-model-auto-cross-testing-123-final-pipes-value-error-solve
  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-electra-model-auto-cross-testing-123-final-pipes-value-error-solve

This model is a fine-tuned version of [rv2307/electra-small-ner](https://huggingface.co/rv2307/electra-small-ner) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7183
- Accuracy: 0.2727

## 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 180  | 1.7288          | 0.2429   |
| No log        | 2.0   | 360  | 1.7230          | 0.2502   |
| 1.7009        | 3.0   | 540  | 1.7216          | 0.2676   |
| 1.7009        | 4.0   | 720  | 1.7216          | 0.2705   |
| 1.7009        | 5.0   | 900  | 1.7183          | 0.2748   |
| 1.6754        | 6.0   | 1080 | 1.7186          | 0.2748   |
| 1.6754        | 7.0   | 1260 | 1.7178          | 0.2741   |
| 1.6754        | 8.0   | 1440 | 1.7184          | 0.2748   |
| 1.6715        | 9.0   | 1620 | 1.7183          | 0.2741   |
| 1.6715        | 10.0  | 1800 | 1.7183          | 0.2727   |


### Framework versions

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