Cruciator
/

bert2bert-dialogsum

@@ -1,199 +1,108 @@
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 ---
+license: apache-2.0
+language:
+- en
+base_model: bert-base-uncased
+tags:
+- encoder-decoder
+- text2text-generation
+- summarization
+- dialogue-summarization
+datasets:
+- knkarthick/dialogsum
+metrics:
+- rouge
+pipeline_tag: text2text-generation
 ---
+# bert2bert-dialogsum
+This is a BERT2BERT encoder-decoder model, warm-started from two `bert-base-uncased` checkpoints and fine-tuned for **dialogue summarization** on the [DialogSum](https://huggingface.co/datasets/knkarthick/dialogsum) dataset.
 ## Model Details
 ### Model Description
+The model takes a multi-turn dialogue as input and generates a short, single-sentence-style summary of the conversation. Both the encoder and decoder are initialized from `bert-base-uncased`; a randomly initialized cross-attention module was added to the decoder to let it attend over the encoder's output, following the standard [Leveraging Pre-trained Checkpoints](https://arxiv.org/abs/1907.12461) warm-start recipe for encoder-decoder models.
+- **Developed by:** Gaurav
+- **Model type:** Encoder-decoder (BERT2BERT) for text2text generation
+- **Language:** English
+- **License:** Apache 2.0 (inherited from the base `bert-base-uncased` checkpoints)
+- **Finetuned from model:** [bert-base-uncased](https://huggingface.co/bert-base-uncased) (both encoder and decoder)
+### Model Sources
+- **Base model:** [bert-base-uncased](https://huggingface.co/bert-base-uncased)
+- **Training data:** [knkarthick/dialogsum](https://huggingface.co/datasets/knkarthick/dialogsum)
 ## Uses
 ### Direct Use
+Summarizing short, informal, multi-turn dialogues (e.g. chat transcripts, meeting snippets, customer support conversations) into a concise summary. Use the `text2text-generation` / `EncoderDecoderModel.generate()` API with an input dialogue truncated to 256 tokens.
 ### Out-of-Scope Use
+- Long-form documents, articles, or single-speaker text (the model was trained exclusively on short multi-turn dialogues, capped at 256 input tokens)
+- Non-English text
+- Domains far outside DialogSum's everyday-conversation style (e.g. legal, medical, or highly technical dialogue) without further fine-tuning
 ## Bias, Risks, and Limitations
+- Trained on a relatively small dataset (~12.5k training examples) for a small number of epochs, so summary quality and factual faithfulness are not guaranteed — always review generated summaries before relying on them.
+- Inherits any biases present in `bert-base-uncased` and in the DialogSum dataset's crowd-sourced dialogues.
+- Input dialogues are truncated at 256 tokens and output summaries are capped at 64 tokens, so longer conversations will be cut off rather than summarized in full.
 ## How to Get Started with the Model
+```python
+from transformers import EncoderDecoderModel, BertTokenizerFast
+model = EncoderDecoderModel.from_pretrained("Cruciator/bert2bert-dialogsum")
+tokenizer = BertTokenizerFast.from_pretrained("Cruciator/bert2bert-dialogsum")
+dialogue = "#Person1#: Can we reschedule our meeting to Friday? #Person2#: Sure, works for me."
+inputs = tokenizer(dialogue, return_tensors="pt", truncation=True, max_length=256)
+summary_ids = model.generate(**inputs)
+print(tokenizer.decode(summary_ids[0], skip_special_tokens=True))
+```
 ## Training Details
 ### Training Data
+[DialogSum](https://huggingface.co/datasets/knkarthick/dialogsum) — 12,460 training examples and 500 validation examples of short, multi-turn dialogues paired with human-written summaries.
 ### Training Procedure
+- Inputs tokenized to a max length of 256 tokens; targets tokenized to a max length of 64 tokens
+- Both encoder and decoder warm-started from `bert-base-uncased`; cross-attention weights randomly initialized
+- Decoder inputs generated internally from labels (standard right-shifted teacher forcing)
 #### Training Hyperparameters
+- **Epochs:** 3
+- **Per-device train/eval batch size:** 16
+- **Mixed precision:** fp16
+- **Generation (eval-time):** beam search, `num_beams=2`
+- **Generation (inference defaults on this checkpoint):** beam search, `num_beams=4`, `length_penalty=2.0`, `no_repeat_ngram_size=3`, `early_stopping=True`
+- **Hardware:** Google Colab, NVIDIA T4 GPU
+### Evaluation
+Evaluated on the DialogSum validation split (500 examples) at the end of each epoch using ROUGE-2 F-measure.
+| Epoch | Training Loss | Validation Loss | ROUGE-2 F-measure |
+|:-----:|:--------------:|:----------------:|:------------------:|
+| 1     | 2.489          | 2.257            | 0.0612              |
+| 2     | 2.072          | 2.091            | 0.0884              |
+| 3     | 1.734          | 2.070            | 0.1069              |
+ROUGE-2 was still improving at the final epoch, suggesting further training beyond 3 epochs would likely improve results further.
+## Technical Specifications
+- **Model size:** ~0.2B parameters
+- **Tensor type:** F32
+- **Framework:** 🤗 Transformers (v5)