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--- |
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license: mit |
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base_model: roberta-base |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: storyseeker |
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results: [] |
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--- |
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# 🔭StorySeeker |
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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. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4343 |
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- Accuracy: 0.8416 |
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## Citation |
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If you use our data, codebook, or models, please cite the following preprint: |
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[Where do people tell stories online? Story Detection Across Online Communities](https://github.com/maria-antoniak/storyseeker/blob/main/2024_where_are_stories_preprint.pdf) |
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Maria Antoniak, Joel Mire, Maarten Sap, Elliott Ash, Andrew Piper |
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## Model description |
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This model can be used to predict whether a text contains or does not contain a story. |
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For our definition of "story" please refer to our [codebook](https://github.com/maria-antoniak/storyseeker). |
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## Quick Start with Colab |
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You can view a demonstration of how to load our annotations, fetch the texts, load our fine-tuned model from Hugging Face, and run predictions. If you use the Colab link, you don't need to download anything or set up anything on your local machine; everything will run in your internet browser. |
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Colab: [link](https://colab.research.google.com/drive/11WJx97FbQELMmQSXbayeJ-gUJyYjCyAv?usp=sharing) |
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Github: [link](https://github.com/maria-antoniak/storyseeker/blob/main/storyseeker_demo.ipynb) |
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## Intended uses & limitations |
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This model is intended for researchers interested in measuring storytelling in online communities, though it can be applied to other kinds of datasets (see generalization results in our preprint). |
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## Training and evaluation data |
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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). |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 20 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 20 |
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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 0.6969 | 0.53 | 10 | 0.7059 | 0.4158 | |
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| 0.6942 | 1.05 | 20 | 0.6674 | 0.6139 | |
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| 0.602 | 1.58 | 30 | 0.4691 | 0.7921 | |
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| 0.4826 | 2.11 | 40 | 0.4711 | 0.7921 | |
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| 0.2398 | 2.63 | 50 | 0.4685 | 0.8119 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.0+cu121 |
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- Tokenizers 0.15.2 |
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