| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: answerdotai/ModernBERT-base |
| tags: |
| - generated_from_trainer |
| model-index: |
| - name: bin |
| results: [] |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # bin |
|
|
| This model is a fine-tuned version of [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.1729 |
| - Mse: 0.1729 |
|
|
| ## Model description |
|
|
| This is a modernbert model with a regression head designed to predict the Content score of a summary. |
|
|
| The input should be the summary + [sep] + source. |
|
|
| ``` |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification |
| |
| model = AutoModelForSequenceClassification.from_pretrained("wesleymorris/modernbert-content", num_labels=1) |
| tokenizer = AutoTokenizer.from_pretrained("wesleymorris/modernbert-content") |
| |
| def get_score(summary: str, |
| source: str): |
| text = summary+tokenizer.sep_token+source |
| inputs = tokenizer(text, return_tensors = 'pt') |
| return float(model(**inputs).logits[0]) |
| ``` |
|
|
|
|
| ### Corpus |
| It was trained on a corpus of 4,233 summaries of 101 sources compiled by Botarleanu et al. (2022). |
| The summaries were graded by expert raters on 6 criteria: Details, Main Point, Cohesion, Paraphrasing, Objective Language, and Language Beyond the Text. |
| A principle component analyis was used to reduce the dimensionality of the outcome variables to two. |
|
|
| Content includes Details, Main Point, Paraphrasing and Cohesion |
|
|
| ### Contact |
| This model was developed by LEAR Lab at Vanderbilt University. For questions or comments about this model, please contact wesley.g.morris@vanderbilt.edu. |
|
|
| ## Intended uses & limitations |
|
|
| This model can be used to predict human scores of content for a summary. |
| The scores are normalized such that 0 is the mean of the training data and 1 is one standard deviation from the mean. |
|
|
| ## Training and evaluation data |
|
|
| Before the finetuning step, the model was pretrained on a very large synthetic dataset. |
|
|
| ## Training procedure |
|
|
| ### Training hyperparameters |
|
|
| The following hyperparameters were used during training: |
| - learning_rate: 5e-05 |
| - train_batch_size: 8 |
| - eval_batch_size: 16 |
| - seed: 42 |
| - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| - lr_scheduler_type: linear |
| - lr_scheduler_warmup_steps: 100 |
| - num_epochs: 10 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Mse | |
| |:-------------:|:-----:|:----:|:---------------:|:------:| |
| | No log | 1.0 | 411 | 0.3181 | 0.3181 | |
| | 0.5319 | 2.0 | 822 | 0.2884 | 0.2884 | |
| | 0.2343 | 3.0 | 1233 | 0.2395 | 0.2395 | |
| | 0.1366 | 4.0 | 1644 | 0.1885 | 0.1885 | |
| | 0.0688 | 5.0 | 2055 | 0.1896 | 0.1896 | |
| | 0.0688 | 6.0 | 2466 | 0.1854 | 0.1854 | |
| | 0.0417 | 7.0 | 2877 | 0.1738 | 0.1738 | |
| | 0.0201 | 8.0 | 3288 | 0.1759 | 0.1759 | |
| | 0.0086 | 9.0 | 3699 | 0.1800 | 0.1800 | |
| | 0.0037 | 10.0 | 4110 | 0.1729 | 0.1729 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.48.3 |
| - Pytorch 2.6.0+cu124 |
| - Datasets 3.2.0 |
| - Tokenizers 0.21.0 |
| |
