yousefg
/

Academ-0.5

@@ -18,6 +18,7 @@ pipeline_tag: summarization
 <!-- Provide a quick summary of what the model is/does. -->
 Academ is a fine-tuned BART model for summarizing academic lectures.
 ## Model Details
@@ -28,61 +29,20 @@ Academ is a fine-tuned BART model for summarizing academic lectures.
 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
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
 - **Model type:** Summarization
 - **Language(s) (NLP):** English
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** BART Large
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-[More Information Needed]
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases, and limitations of the model. More information needed for further recommendations.
 ## How to Get Started with the Model
 Use the code below to get started with the model.
 def get_summary(input_ids, attention_mask, context_length):
     summaries = []
@@ -106,135 +66,31 @@ attention_mask = torch.tensor(batch['attention_mask']).unsqueeze(0).to(device)
 summary = get_summary(input_ids, attention_mask, 1654)
 print(summary)
-[More Information Needed]
 ## Training Details
-### Training Data
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-[More Information Needed]
-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
-[More Information Needed]
 #### Training Hyperparameters
-- **Training regime:** [More Information Needed] 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
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-[More Information Needed]
 ## 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, Factors & Metrics
 #### Testing Data
 The model's test dataset had 289 lectures, mainly from MIT OpenCourseWare.
 <!-- This should link to a Dataset Card if possible. -->
-[More Information Needed]
-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-[More Information Needed]
-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-[More Information Needed]
 ### Results
-[More Information Needed]
 #### Summary
-Academ is a summarization model trained on 2307 lectures, mainly from MIT OpenCourseWare. The model has a max sequence length of 1654, an increase of 630 tokens from the original model.
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
-[More Information Needed]
-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
-### Model Architecture and Objective
-[More Information Needed]
-### Compute Infrastructure
-[More Information Needed]
-#### Hardware
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-#### Software
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-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
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-**APA:**
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-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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-## More Information [optional]
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-## Model Card Authors [optional]
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-## Model Card Contact
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 <!-- 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
 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 = []
 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.