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tiedaar / language-beyond-the-source
Model's Last Updated: July 03 2024
text-classification
Introduction of language-beyond-the-source
Model Details of language-beyond-the-source
Language Beyond the Source
Model description
This model is a fine-tuned version of microsoft/deberta-v3-large on a dataset consisting of 4,620 summaries, scored on an analytic rubric by expert raters. This model predicts the raw score for Language Beyond the Source. The rubric is as follows:
LANGUAGE BEYOND THE SOURCE
- 1 Point: Summary shows a very basic understanding of lexical and syntactic structures.
- 2 Points: Summary shows an understanding of lexical and syntactic structures.
- 3 Points: Summary shows an appropriate range of lexical and syntactic structures.
- 4 Points: Summary shows an excellent range of lexical and syntactic structures.
It achieves the following results on the evaluation set:
- Loss: 0.1817
- Mse: 0.1817
- Rmse: 0.4263
On set of summaries of sources that were withheld from the training set, the model achieved the following results:
- Rmse: 0.4220
- R2: 0.6236
Intended uses & limitations
This model is intended to be used to provide feedback to users of iTELL, a framework for generating intelligent educational texts.
For more information about iTELL, watch our video here:
Training and evaluation data
Seventy summaries in the training set had Language Beyond the Source scores of <1, which is outside of the rubric. These summaries were removed from the training and test sets.
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 8.5e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 7
Training results
| Training Loss | Epoch | Step | Validation Loss | Mse | Rmse |
|---|---|---|---|---|---|
| No log | 1.0 | 405 | 0.1901 | 0.1901 | 0.4360 |
| 0.5772 | 2.0 | 810 | 0.2181 | 0.2181 | 0.4670 |
| 0.1498 | 3.0 | 1215 | 0.2259 | 0.2259 | 0.4752 |
| 0.0969 | 4.0 | 1620 | 0.1845 | 0.1845 | 0.4296 |
| 0.0587 | 5.0 | 2025 | 0.1657 | 0.1657 | 0.4071 |
| 0.0587 | 6.0 | 2430 | 0.1731 | 0.1731 | 0.4161 |
| 0.0397 | 7.0 | 2835 | 0.1817 | 0.1817 | 0.4263 |
Framework versions
- Transformers 4.40.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
Contact
This model was developed by LEAR Lab at Vanderbilt University. For questions or comments about this model, please contact [email protected] .
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Updated:November 28 2022
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