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---
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 [email protected].
## 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
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