File size: 2,938 Bytes
69e36dd fac6433 69e36dd ff88f09 69e36dd 4c0c633 69e36dd 4c0c633 69e36dd ff88f09 69e36dd ff88f09 69e36dd a1e592a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 |
---
license: mit
tags:
- generated_from_trainer
base_model: microsoft/deberta-v3-large
model-index:
- name: grammar_checkpoints
results: []
---
# Language Beyond the Source
## Model description
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co./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: [](https://www.youtube.com/watch?v=YZXVQjSDZtI)
## 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]]([email protected]).
|