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language: en |
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license: apache-2.0 |
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tags: |
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- summarization |
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- conversational-ai |
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- text2text-generation |
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- t5 |
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datasets: |
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- knkarthick/samsum |
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metrics: |
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- rouge |
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base_model: |
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- google-t5/t5-small |
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--- |
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# 470 Final Project Model -> Summary Model |
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## Model Overview |
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This repository contains a fine-tuned **T5-small** model for **abstractive conversational text summarization**. |
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Given a multi-speaker dialogue, the model generates a concise natural-language summary that captures the main points of the conversation. |
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- **Base model:** google-t5/t5-small |
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- **Task:** Abstractive text summarization |
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- **Model type:** Encoder–decoder transformer (T5) |
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--- |
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## Dataset |
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The model was fine-tuned on the **SAMSum** dataset, which consists of chat-style conversations paired with human-written summaries. |
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- **Dataset name:** knkarthick/samsum |
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- **Fields:** |
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- `dialogue`: conversation text (input) |
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- `summary`: reference summary (target) |
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- **Splits:** train / validation / test |
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--- |
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## Training Details |
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- **Epochs:** 3 |
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- **Learning rate:** 3e-4 |
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- **Batch size:** 8 |
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- **Max input length:** 512 tokens |
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- **Max target length:** 128 tokens |
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- **Training framework:** Hugging Face Transformers (`Seq2SeqTrainer`) |
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- **Hardware:** GPU (Google Colab) |
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--- |
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## Evaluation |
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The model was evaluated on the test split of the SAMSum dataset using ROUGE metrics. |
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- **ROUGE-1:** 0.4538 |
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- **ROUGE-2:** 0.2123 |
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- **ROUGE-L:** 0.3762 |
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(Replace the values with the scores obtained in the notebook.) |
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## Intended Uses |
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This model can be used for: |
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- Summarizing chat conversations or dialogues |
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- Demonstrations of abstractive summarization |
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- Educational purposes in NLP and machine learning |
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--- |
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## Limitations |
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- The model may omit important details in long or complex conversations. |
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- Generated summaries may occasionally be imprecise or incomplete. |
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- The model is trained on informal dialogue and may not generalize well to other domains. |
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--- |
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## How to Use |
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```python |
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
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repo_id = "marvingoenner/470finalprojectmodel" |
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tokenizer = AutoTokenizer.from_pretrained(repo_id) |
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model = AutoModelForSeq2SeqLM.from_pretrained(repo_id) |
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dialogue = "Amanda: I baked cookies. Jerry: Sounds great! Amanda: I will bring some tomorrow." |
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inputs = tokenizer("summarize: " + dialogue, return_tensors="pt", truncation=True) |
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output_ids = model.generate(**inputs, max_new_tokens=64) |
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print(tokenizer.decode(output_ids[0], skip_special_tokens=True)) |
