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pszemraj / karnold-walmer-base-biopapers
Model's Last Updated: 2025年12月29日
text-generation
Introduction of karnold-walmer-base-biopapers
Model Details of karnold-walmer-base-biopapers
karnold-walmer-base-biopapers
Karnold-Walmer is a text2text model based on
google/long-t5-tglobal-base
, specifically designed to decode the 'keywords' column of
pszemraj/scientific_lay_summarisation-plos-norm
.
Karnold-Walmer focuses on extracting relevant keywords from the input text, making it a powerful tool for keyword identification and text classification. It was fine-tuned on & supports text input of up to 16,384 tokens.
It achieves the following results on the evaluation set:
- Loss: 0.8844
- Rouge1: 46.7593
- Rouge2: 28.3538
- Rougel: 42.2921
- Rougelsum: 42.2774
- Gen Len: 78.1706
Intended Uses & Limitations
Karnold-Walmer is intended to be used for keyword extraction and text classification in various domains, such as scientific literature, biomedical research articles, and more. By analyzing the content of an input text, the model generates a list of relevant keywords that describe the topic of the article.
It is important to note, however, that Karnold-Walmer is specifically trained to decode text similar to the "keywords" column and is not designed for summarization tasks. For accurate keyword extraction and text classification, the model should be used within the limits of its training data and intended purpose (see what happens when you try the out-of-domain API examples).
Training and Evaluation Data
Karnold-Walmer was trained on the PLOS dataset, which contains full biomedical research articles paired with expert-written lay summaries and keyword lists. The model was tuned to decode the "keywords" column in the dataset, focusing on keyword extraction and text classification tasks.
Wordcloud
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0004
- train_batch_size: 4
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 2.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| 2.0471 | 0.15 | 100 | 1.6138 | 12.4374 | 4.1861 | 11.1863 | 11.1833 | 324.6971 |
| 1.5654 | 0.3 | 200 | 1.3447 | 23.9982 | 11.1431 | 21.4173 | 21.4413 | 176.0294 |
| 1.3467 | 0.45 | 300 | 1.2038 | 33.8084 | 18.1588 | 30.4748 | 30.4142 | 107.7735 |
| 1.4398 | 0.6 | 400 | 1.1054 | 37.772 | 20.8967 | 33.859 | 33.8324 | 102.9029 |
| 1.306 | 0.75 | 500 | 1.0478 | 39.2642 | 22.0388 | 35.6578 | 35.5773 | 91.1235 |
| 1.1677 | 0.9 | 600 | 0.9994 | 40.5149 | 22.8507 | 36.3888 | 36.3499 | 103.9118 |
| 1.078 | 1.05 | 700 | 0.9627 | 42.301 | 24.2523 | 38.0739 | 38.0532 | 88.4941 |
| 1.0942 | 1.2 | 800 | 0.9443 | 44.5907 | 26.2046 | 39.7461 | 39.6763 | 88.7559 |
| 1.0209 | 1.35 | 900 | 0.9108 | 45.357 | 26.861 | 40.6411 | 40.706 | 90.1206 |
| 1.1161 | 1.5 | 1000 | 0.9026 | 47.1362 | 28.6605 | 42.6406 | 42.6108 | 79.2412 |
| 1.1224 | 1.65 | 1100 | 0.8907 | 47.31 | 28.4395 | 42.6658 | 42.6509 | 78.4265 |
| 0.9857 | 1.8 | 1200 | 0.8862 | 46.7061 | 28.1586 | 42.3181 | 42.3105 | 80.5059 |
| 1.0011 | 1.95 | 1300 | 0.8844 | 46.7593 | 28.3538 | 42.2921 | 42.2774 | 78.1706 |
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Updated:2025年12月29日
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