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README.md

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+---
+
+library_name: transformers
+base_model: facebook/bart-large
+pipeline_tag: text2text-generation
+tags:
+- bart
+- title-generation
+- scientific-text
+- summarization
+- scihigh-2026
+- fire-2026
+
+
+---
+
+# SciHigh 2026 Task 2 — BART-large Title Generation Model
+
+This repository contains a fine-tuned **BART-large** model for **Task 2: Title Generation from Abstracts** of the SciHigh 2026 shared task at FIRE 2026.
+
+The model takes a scientific paper abstract as input and generates a concise research-paper title.
+
+## Base Model
+
+* Model: `facebook/bart-large`
+* Architecture: BART encoder-decoder Transformer
+* Parameters: approximately 406 million
+* Framework: Hugging Face Transformers
+
+## Task
+
+**Input:** Scientific paper abstract
+**Output:** Generated scientific paper title
+
+The model was fine-tuned for highly compressed abstractive generation, where the goal is to capture the central topic and contribution of an abstract in title form.
+
+## Dataset
+
+The model was fine-tuned on the **SpringerSSAT** dataset released for SciHigh 2026 Task 2.
+
+Dataset split used:
+
+* Training: 2,778 abstract-title pairs
+* Validation: 347 abstract-title pairs
+* Test: 348 abstracts with masked reference titles
+
+Only the training split was used for gradient updates. The validation set was used for evaluation and best-checkpoint selection.
+
+## Preprocessing
+
+* Maximum abstract length: 512 tokens
+* Maximum target-title length: 64 tokens
+* Input truncation: enabled
+* Target truncation: enabled
+* Dynamic batch padding using `DataCollatorForSeq2Seq`
+* No task-specific instruction prefix was added
+
+## Fine-Tuning Configuration
+
+* Epochs: 3
+* Learning rate: `3e-5`
+* Weight decay: `0.01`
+* Per-device training batch size: 1
+* Per-device evaluation batch size: 1
+* Gradient accumulation steps: 8
+* Effective training batch size: 8
+* Mixed-precision training: FP16 AMP
+* Gradient checkpointing: enabled
+* Evaluation strategy: once per epoch
+* Checkpoint saving strategy: once per epoch
+* Generation beam size during validation: 4
+* Maximum generation length: 64
+* Best-model selection metric: ROUGE-L
+* Best model automatically restored after training
+
+## Validation Results
+
+| Epoch | Validation Loss |     ROUGE-1 |     ROUGE-2 |     ROUGE-L |  ROUGE-Lsum |
+| ----- | --------------: | ----------: | ----------: | ----------: | ----------: |
+| 1     |        2.282688 |     44.9926 |     21.3081 |     36.6377 |     36.6539 |
+| 2     |    **2.217401** | **45.7446** | **22.2135** | **37.6433** | **37.6641** |
+| 3     |        2.242662 |     45.2856 |     21.8391 |     37.2244 |     37.2773 |
+
+### Best Checkpoint
+
+The best model was obtained at **Epoch 2**.
+
+* ROUGE-1: **45.7446**
+* ROUGE-2: **22.2135**
+* ROUGE-L: **37.6433**
+* ROUGE-Lsum: **37.6641**
+* Validation loss: **2.217401**
+
+A separate evaluation over all 347 validation examples reproduced these results.
+
+These are validation-set results and are not official hidden-test scores.
+
+## Example Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+model_name = "kckrish21/SciHigh2026-Task2-BART-large"
+
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+
+abstract = """
+Insert a scientific paper abstract here.
+"""
+
+inputs = tokenizer(
+    abstract,
+    return_tensors="pt",
+    max_length=512,
+    truncation=True
+)
+
+generated_ids = model.generate(
+    **inputs,
+    max_length=64,
+    num_beams=4,
+    early_stopping=True
+)
+
+title = tokenizer.decode(
+    generated_ids[0],
+    skip_special_tokens=True
+)
+
+print(title)
+```
+
+## Test-Set Generation
+
+For SciHigh 2026 test inference:
+
+* Test examples: 348
+* Generation batch size: 4
+* Beam size: 4
+* Maximum generation length: 64
+* Early stopping: enabled
+* Missing predictions: 0
+* Empty predictions: 0
+
+The reference test titles are masked by the task organizers, so no test-set metric is reported here.
+
+## Intended Use
+
+This model is intended for:
+
+* scientific paper title generation from abstracts
+* research on scientific text generation
+* participation and reproducibility for SciHigh 2026 Task 2
+
+Generated titles should be treated as model suggestions rather than authoritative paper titles.
+
+## Limitations
+
+The model was fine-tuned on a relatively small dataset of social-science research abstracts. Performance may differ for scientific domains or writing styles that are substantially different from the SpringerSSAT training distribution.
+
+Like other neural text-generation systems, the model may generate wording that is incomplete, overly generic, or not fully supported by the abstract.
+
+## Shared Task
+
+**SciHigh 2026 — Research Highlight Generation from Scientific Papers**
+**FIRE 2026**
+Subtask 2: **Title Generation from Abstracts**
+
+Ranking for the shared task is primarily based on ROUGE-L F1.