---
library_name: transformers
tags:
- lecture
- college
- university
- summarization
license: mit
language:
- en
metrics:
- rouge
pipeline_tag: summarization
---

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->
Academ is a fine-tuned BART model for summarizing academic lectures. 

To find out how the model was fine-tuned, you can check the notebook on Kaggle: https://www.kaggle.com/code/yousefr/college-lectures-summarization-bart-unsupervised/

## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

- **Developed by:** Yousef Gamaleldin
- **Model type:** Summarization
- **Language(s) (NLP):** English
- **License:** MIT
- **Finetuned from model [optional]:** BART Large CNN

## How to Get Started with the Model

Use the code below to get started with the model.

```
from transformers import BartForConditionalGeneration, AutoTokenizer

model = BartForConditionalGeneration.from_pretrained('yousefg/Academ-0.5')
tokenizer = AutoTokenizer.from_pretrained('facebook/bart-large-cnn')

def get_summary(input_ids, attention_mask, context_length):
    
    summaries = []
    for i in range(0, input_ids.shape[1], context_length):
        
        input_slice = input_ids[:, i:i + context_length] if i + context_length <= input_ids.size(1) else input_ids[:, i:]
        attention_mask_slice = attention_mask[:, i:i + context_length] if i + context_length <= attention_mask.size(1) else attention_mask[:, i:]
        
        summary = model.generate(input_slice, attention_mask = attention_mask_slice, max_new_tokens = 1654, min_new_tokens = 250, do_sample = True, renormalize_logits = True)
        summaries.extend(summary[0].tolist())
        
    summaries = tokenizer.decode(summaries, skip_special_tokens = True)
    
    return summaries
    
batch = tokenizer(texts, truncation = False) # make sure to get the transcript from the lecture

input_ids = torch.tensor(batch['input_ids']).unsqueeze(0).to(device)
attention_mask = torch.tensor(batch['attention_mask']).unsqueeze(0).to(device)

summary = get_summary(input_ids, attention_mask, 1654)
print(summary)
```

## Training Details

The model's training used a custom loss function for getting the model into an optimal length (35% chosen as the optimal length).

#### Training Hyperparameters

- **Training regime:** bf16 non-mixed precision<!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
- **Learning Rate:** 0.001
- **Weight Decay:** 0.01
- **Epochs:** 4
- **Batch Size:** 16
- 
## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->
The evaluation is based on ROUGE 1 with a change of discounting padding tokens. 

#### Testing Data

The model's test dataset had 289 lectures, mainly from MIT OpenCourseWare.
<!-- This should link to a Dataset Card if possible. -->

### Results

The model achieved 96% accuracy for ROUGUE 1 in the test dataset, and 93% in the evaluation dataset.

#### Summary
Academ is a summarization model trained on 2307 lectures, mainly from MIT OpenCourseWare. The model has a max sequence length of 1654, increasing 630 tokens from the original model.