--- library_name: transformers license: apache-2.0 base_model: answerdotai/ModernBERT-base tags: - generated_from_trainer model-index: - name: bin results: [] --- # 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