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ambrosfitz
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summarize-grammar

Summarization
Safetensors
English
t5
grammar-enhanced
Eval Results
Model card Files Community
summarize-grammar
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---
language: en
license: apache-2.0
tags:
- t5
- summarization
- grammar-enhanced
datasets:
- ambrosfitz/grammar-summary
model-index:
- name: Grammar-Enhanced T5 Summarizer
  results:
  - task:
      name: Text Summarization
      type: summarization
    dataset:
      name: ambrosfitz/grammar-summary
      type: ambrosfitz/grammar-summary
    metrics:
      - name: Validation Loss
        type: loss
        value: 0.8700
      - name: Model Type
        type: metric
        value: T5-base
---

# Grammar-Enhanced T5 Summarizer

This model is a fine-tuned version of T5-base for text summarization with grammar-enhanced inputs. It was trained on historical text summaries with explicit grammar structure analysis.

## Model Description

- **Base Model**: T5-base
- **Task**: Text Summarization
- **Training Data**: Historical texts with grammar analysis
- **Input Format**: Structured text with grammar analysis (subjects, verbs, objects, relationships)
- **Output Format**: Concise summary

## Usage

```python
from transformers import T5ForConditionalGeneration, T5Tokenizer

# Load model and tokenizer
model = T5ForConditionalGeneration.from_pretrained("ambrosfitz/summarize-grammar")
tokenizer = T5Tokenizer.from_pretrained("ambrosfitz/summarize-grammar")

# Prepare input
text = "Your text here..."
input_text = f"summarize: {text}"

# Generate summary
inputs = tokenizer(input_text, return_tensors="pt", max_length=512, truncation=True)
outputs = model.generate(**inputs, max_length=150, num_beams=4, length_penalty=2.0)
summary = tokenizer.decode(outputs[0], skip_special_tokens=True)
```

## Training Details

The model was fine-tuned on a dataset of historical texts with additional grammar analysis information. Each input includes:
- Main subjects
- Key verbs
- Objects
- Grammatical relationships

The model achieved a validation loss of 0.8700 during training.

## Limitations

This model works best with:
- Historical texts
- Formal writing
- English language content
- Texts that benefit from structural analysis

## Citation

If you use this model, please cite:
```
@misc{grammar-t5-summarizer,
  author = {repo_owner},
  title = {Grammar-Enhanced T5 Summarizer},
  year = {2024},
  publisher = {Hugging Face},
  journal = {Hugging Face Model Hub},
  howpublished = {https://huggingface.co/ambrosfitz/summarize-grammar}
}
```