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  library_name: transformers
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- tags: []
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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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  ## 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 the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- - **Developed by:** [More Information Needed]
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- - **Funded by [optional]:** [More Information Needed]
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- - **Shared by [optional]:** [More Information Needed]
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- - **Model type:** [More Information Needed]
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- - **Language(s) (NLP):** [More Information Needed]
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  - **License:** [More Information Needed]
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- - **Finetuned from model [optional]:** [More Information Needed]
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- ### Model Sources [optional]
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- <!-- Provide the basic links for the model. -->
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- - **Repository:** [More Information Needed]
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- - **Paper [optional]:** [More Information Needed]
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- - **Demo [optional]:** [More Information Needed]
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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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  ### 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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- [More Information Needed]
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  ### Downstream Use [optional]
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- <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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- [More Information Needed]
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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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- [More Information Needed]
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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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- [More Information Needed]
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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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- [More Information Needed]
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- ## Training Details
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- ### Training Data
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- <!-- 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. -->
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- [More Information Needed]
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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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- #### Preprocessing [optional]
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- [More Information Needed]
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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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- #### 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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- [More Information Needed]
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- ## Evaluation
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- <!-- This section describes the evaluation protocols and provides the results. -->
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- ### Testing Data, Factors & Metrics
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- #### Testing Data
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- <!-- This should link to a Dataset Card if possible. -->
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- [More Information Needed]
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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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- ### Results
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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:** [More Information Needed]
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- - **Hours used:** [More Information Needed]
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- - **Cloud Provider:** [More Information Needed]
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- - **Compute Region:** [More Information Needed]
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- - **Carbon Emitted:** [More Information Needed]
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- ## Technical Specifications [optional]
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- ### Model Architecture and Objective
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- ### Compute Infrastructure
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- #### Hardware
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- #### Software
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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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- **APA:**
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- ## Glossary [optional]
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- [More Information Needed]
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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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- [More Information Needed]
 
 
 
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  library_name: transformers
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+ tags: [text-summarization, pegasus, SAMSum, seq2seq]
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+ # Model Card for mikemayuare/text-summarizer
 
 
 
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+ This model is fine-tuned on the SAMSum dataset and is designed for text summarization tasks. It is built on top of the `google/pegasus-cnn_dailymail` base model. The model is intended for sequence-to-sequence summarization tasks and should be loaded with the `AutoModelForSeq2SeqLM` class from the Hugging Face Transformers library.
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  ## Model Details
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  ### Model Description
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+ This is a 🤗 transformers model fine-tuned on the SAMSum dataset. The base model is `google/pegasus-cnn_dailymail`, which is optimized for summarizing CNN and Daily Mail articles. The SAMSum dataset consists of conversations, making this model especially suited for summarizing dialogue or chat-based data.
 
 
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+ - **Developed by:** Miguelangel Leon
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+ - **Funded by:** This is a personal project, not funded.
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+ - **Shared by:** Miguelangel Leon
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+ - **Model type:** Sequence-to-Sequence (Text Summarization)
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+ - **Language(s) (NLP):** English
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  - **License:** [More Information Needed]
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+ - **Finetuned from model:** google/pegasus-cnn_dailymail
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+ ### Model Sources
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+ - **Repository:** [GitHub Repository](https://github.com/mikemayuare/text-summarizer)
 
 
 
 
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  ## Uses
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  ### Direct Use
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+ This model is designed to summarize dialogues or conversational text. It works well for summarizing conversations into concise summaries, as provided in the SAMSum dataset.
 
 
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  ### Downstream Use [optional]
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+ This model can be fine-tuned further for other types of text summarization tasks, such as summarizing customer support chats or informal conversations in other contexts.
 
 
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  ### Out-of-Scope Use
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+ This model is not optimized for document summarization of long, formal texts like research papers, books, or non-conversational news articles.
 
 
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  ## Bias, Risks, and Limitations
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+ As the model is fine-tuned on conversational data from the SAMSum dataset, it may not generalize well to all kinds of conversations, particularly those outside the training distribution. The SAMSum dataset is focused on English-language conversations, so the model's performance may degrade when applied to non-English conversations.
 
 
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  ### Recommendations
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+ Users should be cautious when using the model for non-dialogue or non-conversational texts, as the model may produce inaccurate summaries. It is recommended to evaluate the model on your specific dataset before deploying it in production.
 
 
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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 for text summarization:
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ```python
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+ from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
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+ # Load the tokenizer and model
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+ tokenizer = AutoTokenizer.from_pretrained("mikemayuare/text-summarizer")
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+ model = AutoModelForSeq2SeqLM.from_pretrained("mikemayuare/text-summarizer")
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+ # Sample input
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+ text = "Your conversational input text goes here."
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+ # Tokenize and generate a summary
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+ inputs = tokenizer(text, return_tensors="pt", truncation=True)
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+ summary_ids = model.generate(inputs['input_ids'], max_length=150, min_length=40, length_penalty=2.0, num_beams=4, early_stopping=True)
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+ # Decode the summary
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+ summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
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+ print(summary)