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--- |
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license: mit |
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base_model: roberta-large-mnli |
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tags: |
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- book |
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- genre |
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- book title |
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metrics: |
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- f1 |
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widget: |
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- text: The Quantum Chip |
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example_title: Science Fiction & Fantasy |
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- text: One Dollar's Journey |
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example_title: Business & Finance |
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- text: Timmy The Talking Tree |
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example_title: idk fiction |
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- text: The Cursed Canvas |
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example_title: Arts & Design |
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- text: Hoops and Hegel |
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example_title: Philosophy & Religion |
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- text: Overview of Streams in North Dakota |
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example_title: Nature |
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- text: Advanced Topology |
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example_title: Non-fiction/Math |
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- text: Cooking Up Love |
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example_title: Food & Cooking |
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- text: Dr. Doolittle's Extraplanatary Commute |
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example_title: Science & Technology |
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pipeline_tag: text-classification |
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--- |
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--- |
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# roberta-large-mnli for title-genre classification |
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This model is a fine-tuned version of [roberta-large-mnli](https://huggingface.co/roberta-large-mnli) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2758 |
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- F1: 0.5464 |
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## Model description |
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This classifies one or more **genre** labels in a **multi-label** setting for a given book **title**. |
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The 'standard' way of interpreting the predictions is that the predicted labels for a given example are **only the ones with a greater than 50% probability.** |
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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: 6e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 128 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-10 |
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- lr_scheduler_type: linear |
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- num_epochs: 5.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:------:| |
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| 0.3096 | 1.0 | 62 | 0.2862 | 0.3707 | |
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| 0.2863 | 2.0 | 124 | 0.2804 | 0.4422 | |
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| 0.2618 | 3.0 | 186 | 0.2773 | 0.4989 | |
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| 0.2432 | 4.0 | 248 | 0.2764 | 0.5223 | |
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| 0.2241 | 5.0 | 310 | 0.2758 | 0.5464 | |
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### Framework versions |
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- Transformers 4.33.3 |
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- Pytorch 2.2.0.dev20231001+cu121 |
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- Datasets 2.14.5 |
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- Tokenizers 0.13.3 |
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