File size: 2,002 Bytes
ac9fac9 4783d23 ac9fac9 4783d23 ac9fac9 4783d23 ac9fac9 46239ed ac9fac9 46239ed ac9fac9 46239ed ac9fac9 4783d23 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 |
---
license: apache-2.0
tags:
- generated_from_trainer
- Multiple Choice
metrics:
- accuracy
model-index:
- name: bert-base-uncased-Figurative_Language
results: []
language:
- en
pipeline_tag: question-answering
---
# bert-base-uncased-Figurative_Language
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased).
It achieves the following results on the evaluation set:
- Loss: 0.7629
- Accuracy: 0.8124
## Model description
For more information on how it was created, check out the following link: https://github.com/DunnBC22/NLP_Projects/blob/main/Multiple%20Choice/Figurative%20Language/Figurative%20Language%20-%20Multiple%20Choice%20Using%20BERT.ipynb
## Intended uses & limitations
This model is intended to demonstrate my ability to solve a complex problem using technology.
## Training and evaluation data
Dataset Source: https://huggingface.co/datasets/nightingal3/fig-qa
**Histogram of Input Lengths**
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6961 | 1.0 | 539 | 0.6932 | 0.5190 |
| 0.6595 | 2.0 | 1078 | 0.5326 | 0.7214 |
| 0.4647 | 3.0 | 1617 | 0.4604 | 0.7948 |
| 0.2884 | 4.0 | 2156 | 0.6204 | 0.8217 |
| 0.1702 | 5.0 | 2695 | 0.7629 | 0.8124 |
### Framework versions
- Transformers 4.29.1
- Pytorch 2.0.1
- Datasets 2.13.1
- Tokenizers 0.13.3 |