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--- |
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license: apache-2.0 |
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language: |
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- en |
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metrics: |
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- Rouge |
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pipeline_tag: summarization |
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tags: |
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- t5 |
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- t5-small |
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- summarization |
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- medical-research |
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--- |
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# Model Card for Model ID |
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<!-- Provide a quick summary of what the model is/does. --> |
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This model is used to automatically generate title from paragraph. |
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## Model Details |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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This is a text generative model to summarize long abstract text jourals into one liners. These one liners can be used as titles in the journal. |
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- **Developed by:** Tushar Joshi |
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- **Shared by [optional]:** Tushar Joshi |
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- **Model type:** t5-small |
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- **Language(s) (NLP):** English |
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- **License:** Apache 2.0 |
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- **Finetuned from model [optional]:** t5-small baseline |
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### Model Sources [optional] |
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<!-- Provide the basic links for the model. --> |
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- **Repository:** https://huggingface.co/t5-small |
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## Uses |
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
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* As a text summarizer to create titles. |
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* As a tunable language model for downstream tasks. |
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### Direct Use |
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> |
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* As a text summarizer for paragraphs. |
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### Out-of-Scope Use |
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> |
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Should not be used as a text summarizer for very long paragraphs. |
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## Bias, Risks, and Limitations |
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<!-- This section is meant to convey both technical and sociotechnical limitations. --> |
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* Max input token size of 1024 |
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* Max output token size of 24 |
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### Recommendations |
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> |
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. |
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## How to Get Started with the Model |
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Use the code below to get started with the model. |
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``` |
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from transformers import pipeline |
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text = """Text that needs to be summarized""" |
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summarizer = pipeline("summarization", model="path-to-model") |
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summary = summarizer(text)[0]["summary_text"] |
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print (summary) |
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``` |
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## Training Details |
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### Training Data |
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<!-- This should link to a Data 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. --> |
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The training data is internally curated and canot be exposed. |
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### Training Procedure |
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> |
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None |
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#### Preprocessing [optional] |
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None |
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#### Training Hyperparameters |
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> |
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- None |
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#### Speeds, Sizes, Times [optional] |
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> |
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The training was done using GPU T4x 2. The task took 4:09:47 to complete. The dataset size of 10,000 examples was used for training the generative model. |
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## Evaluation |
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<!-- This section describes the evaluation protocols and provides the results. --> |
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The quality of summarization was tested on 5000 research journals created over last 20 years. |
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### Testing Data, Factors & Metrics |
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Test Data Size: 5000 examples |
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#### Testing Data |
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<!-- This should link to a Data Card if possible. --> |
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The testing data is internally generated and curated. |
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#### Factors |
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> |
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[More Information Needed] |
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#### Metrics |
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<!-- These are the evaluation metrics being used, ideally with a description of why. --> |
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The model was evaluated on Rouge Metrics below are the baseline results achieved |
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### Results |
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| Epoch | Training Loss | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len| |
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| --- | --- | --- | --- | --- | --- | --- | --- | |
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| 18 | 2.442800 | 2.375408 | 0.313700 | 0.134600 | 0.285400 | 0.285400 | 16.414100 | |
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| 19 | 2.454800 | 2.372553 | 0.312900 | 0.134100 | 0.284900 | 0.285000 | 16.445100 | |
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| 20 | 2.438900 | 2.372551 | 0.312300 | 0.134000 | 0.284500 | 0.284600 | 16.435500 | |
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#### Summary |
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## Model Examination [optional] |
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<!-- Relevant interpretability work for the model goes here --> |
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[More Information Needed] |
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## Environmental Impact |
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> |
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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). |
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- **Hardware Type:** GPU T4 x 2 |
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- **Hours used:** 4.5 |
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- **Cloud Provider:** GCP |
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- **Compute Region:** Ireland |
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- **Carbon Emitted:** Unknown |
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## Technical Specifications [optional] |
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### Model Architecture and Objective |
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[More Information Needed] |
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### Compute Infrastructure |
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[More Information Needed] |
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#### Hardware |
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[More Information Needed] |
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#### Software |
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[More Information Needed] |
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## Citation [optional] |
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> |
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**BibTeX:** |
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[More Information Needed] |
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**APA:** |
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[More Information Needed] |
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## Glossary [optional] |
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<!-- 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 Needed] |
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## More Information [optional] |
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[More Information Needed] |
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## Model Card Authors [optional] |
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Tushar Joshi |
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## Model Card Contact |
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Tushar Joshi |
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LinkedIn - https://www.linkedin.com/in/tushar-joshi-816133100/ |
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