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--- |
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language: en |
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license: mit |
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tags: |
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- summarization |
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- t5 |
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- text-to-text |
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- model |
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- fine-tuned |
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library: transformers |
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task: |
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- text-generation |
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- summarization |
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--- |
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# Fine-tuned T5 Model for Text Summarization |
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This model is a fine-tuned version of the T5 model (`t5-small`) for text summarization tasks. It has been trained on a diverse set of text data to generate concise and coherent summaries from input text. |
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## Model Overview |
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- **Model Type**: T5 (Text-to-Text Transfer Transformer) |
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- **Base Model**: `t5-small` |
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- **Task**: Text Summarization |
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- **Language**: English (other languages may be supported depending on the dataset used) |
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## Intended Use |
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This model is designed to summarize long documents, articles, or any form of textual content into shorter, coherent summaries. It can be used for tasks such as: |
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- Summarizing news articles |
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- Generating abstracts for academic papers |
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- Condensing lengthy documents |
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- Summarizing customer feedback or reviews |
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## Model Details |
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- **Fine-Tuned On**: A custom dataset containing text and corresponding summaries. |
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- **Input**: Text (e.g., news articles, papers, or long-form content) |
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- **Output**: A concise summary of the input text |
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## How to Use |
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To use this model for text summarization, you can follow the code example below: |
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```python |
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from transformers import T5Tokenizer, T5ForConditionalGeneration |
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# Load the fine-tuned model and tokenizer |
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model = T5ForConditionalGeneration.from_pretrained("kawinduwijewardhane/BriefT5") |
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tokenizer = T5Tokenizer.from_pretrained("kawinduwijewardhane/BriefT5") |
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# Input text for summarization |
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input_text = "Your long input text here." |
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# Tokenize and summarize |
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inputs = tokenizer(input_text, return_tensors="pt", max_length=512, truncation=True) |
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summary_ids = model.generate(inputs["input_ids"], max_length=150, 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) |
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``` |
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### Explanation of the YAML metadata: |
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- **`language`**: Specifies the language the model supports, in this case, English (`en`). |
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- **`license`**: Describes the licensing information for your model, here it is set to MIT (you can change it depending on your license). |
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- **`tags`**: These tags help categorize your model on Hugging Face and make it easier for others to discover. I've added tags like `summarization`, `t5`, `text-to-text`, and `fine-tuned`. |
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This will help you resolve the warning and provide the necessary metadata for your model card! |
