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