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---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: storyseeker
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. -->
# storyseeker
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the [StorySeeker](https://github.com/maria-antoniak/storyseeker) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4343
- Accuracy: 0.8416
## Model description
This model can be used to predict whether a text contains or does not contain a story.
For our definition of "story" please refer to our [codebook](https://github.com/maria-antoniak/storyseeker).
## Intended uses & limitations
This model is intended for researchers interested in measuring storytelling in online communities, though it can be applied to other kinds of datasets.
## Training and evaluation data
The model was fine-tuned on the training split of the [StorySeeker](https://github.com/maria-antoniak/storyseeker) dataset, which contains 301 Reddit posts and comments annotated with story and event spans. This model was fine-tuned using binary document labels (the document contains a story or does not contain a story).
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 20
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 20
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6969 | 0.53 | 10 | 0.7059 | 0.4158 |
| 0.6942 | 1.05 | 20 | 0.6674 | 0.6139 |
| 0.602 | 1.58 | 30 | 0.4691 | 0.7921 |
| 0.4826 | 2.11 | 40 | 0.4711 | 0.7921 |
| 0.2398 | 2.63 | 50 | 0.4685 | 0.8119 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Tokenizers 0.15.2
|