marvingoenner
/

470finalprojectmodel

@@ -1,199 +1,91 @@
 ---
-library_name: transformers
-tags: []
 ---
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 ---
+language: en
+license: apache-2.0
+tags:
+- summarization
+- conversational-ai
+- text2text-generation
+- t5
+datasets:
+- knkarthick/samsum
+metrics:
+- rouge
+base_model:
+- google-t5/t5-small
 ---
+# 470 Final Project Model -> Summary Model
+## Model Overview
+This repository contains a fine-tuned **T5-small** model for **abstractive conversational text summarization**.
+Given a multi-speaker dialogue, the model generates a concise natural-language summary that captures the main points of the conversation.
+- **Base model:** google-t5/t5-small
+- **Task:** Abstractive text summarization
+- **Model type:** Encoder–decoder transformer (T5)
+---
+## Dataset
+The model was fine-tuned on the **SAMSum** dataset, which consists of chat-style conversations paired with human-written summaries.
+- **Dataset name:** knkarthick/samsum
+- **Fields:**
+  - `dialogue`: conversation text (input)
+  - `summary`: reference summary (target)
+- **Splits:** train / validation / test
+---
 ## Training Details
+- **Epochs:** 3
+- **Learning rate:** 3e-4
+- **Batch size:** 8
+- **Max input length:** 512 tokens
+- **Max target length:** 128 tokens
+- **Training framework:** Hugging Face Transformers (`Seq2SeqTrainer`)
+- **Hardware:** GPU (Google Colab)
+---
 ## Evaluation
+The model was evaluated on the test split of the SAMSum dataset using ROUGE metrics.
+- **ROUGE-1:** INSERT_YOUR_VALUE
+- **ROUGE-2:** INSERT_YOUR_VALUE
+- **ROUGE-L:** INSERT_YOUR_VALUE
+(Replace the values with the scores obtained in the notebook.)
+---
+## Intended Uses
+This model can be used for:
+- Summarizing chat conversations or dialogues
+- Demonstrations of abstractive summarization
+- Educational purposes in NLP and machine learning
+---
+## Limitations
+- The model may omit important details in long or complex conversations.
+- Generated summaries may occasionally be imprecise or incomplete.
+- The model is trained on informal dialogue and may not generalize well to other domains.
+---
+## How to Use
+```python
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+repo_id = "marvingoenner/470finalprojectmodel"
+tokenizer = AutoTokenizer.from_pretrained(repo_id)
+model = AutoModelForSeq2SeqLM.from_pretrained(repo_id)
+dialogue = "Amanda: I baked cookies. Jerry: Sounds great! Amanda: I will bring some tomorrow."
+inputs = tokenizer("summarize: " + dialogue, return_tensors="pt", truncation=True)
+output_ids = model.generate(**inputs, max_new_tokens=64)
+print(tokenizer.decode(output_ids[0], skip_special_tokens=True))