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README.md

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 ---
-license: mit
+language:
+  - en
+library_name: transformers
+pipeline_tag: summarization
+license: apache-2.0
+tags:
+  - chemistry
+  - scientific-summarization
+  - distilbart
+  - abstractive
+  - tldr
+  - knowledge-graphs
+datasets:
+  - Bocklitz-Lab/lit2vec-tldr-bart-dataset
+model-index:
+- name: lit2vec-tldr-bart
+  results:
+  - task:
+      name: Summarization
+      type: summarization
+    dataset:
+      name: Lit2Vec TL;DR Chemistry Dataset
+      type: Bocklitz-Lab/lit2vec-tldr-bart-dataset
+      split: test
+      size: 1001
+    metrics:
+      - type: rouge1
+        value: 56.11
+      - type: rouge2
+        value: 30.78
+      - type: rougeLsum
+        value: 45.43
 ---
+
+# lit2vec-tldr-bart (DistilBART fine-tuned for chemistry TL;DRs)
+
+**lit2vec-tldr-bart** is a DistilBART model fine-tuned on **19,992** CC-BY licensed chemistry abstracts to produce **concise TL;DR-style summaries** aligned with methods → results → significance. It’s designed for scientific **abstractive summarization**, **semantic indexing**, and **knowledge-graph population** in chemistry and related fields.
+
+- **Base model:** `sshleifer/distilbart-cnn-12-6`
+- **Training data:** [`Bocklitz-Lab/lit2vec-tldr-bart-dataset`](https://huggingface.co/datasets/Bocklitz-Lab/lit2vec-tldr-bart-dataset)
+- **Max input length:** 1024 tokens  
+- **Target length:** ~128 tokens
+
+---
+
+## 🧪 Evaluation (held-out test)
+
+| Split | ROUGE-1 | ROUGE-2 | ROUGE-Lsum |
+|------:|--------:|--------:|-----------:|
+| Test  | **56.11** | **30.78** | **45.43** |
+
+> Validation RLsum: 46.05  
+> Metrics computed with `evaluate`'s `rouge` (NLTK sentence segmentation, `use_stemmer=True`).
+
+---
+
+## 🚀 Quickstart
+
+```python
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, GenerationConfig
+
+repo = "Bocklitz-Lab/lit2vec-tldr-bart"
+
+tok = AutoTokenizer.from_pretrained(repo)
+model = AutoModelForSeq2SeqLM.from_pretrained(repo)
+gen = GenerationConfig.from_pretrained(repo)  # loads default decoding params
+
+text = "Proton exchange membrane fuel cells convert chemical energy into electricity..."
+inputs = tok(text, return_tensors="pt", truncation=True, max_length=1024)
+
+summary_ids = model.generate(**inputs, **gen.to_dict())
+print(tok.decode(summary_ids[0], skip_special_tokens=True))
+````
+
+### Batch inference (PyTorch)
+
+```python
+texts = [
+  "Abstract 1 ...",
+  "Abstract 2 ...",
+]
+batch = tok(texts, return_tensors="pt", padding=True, truncation=True, max_length=1024)
+out = model.generate(**batch, **gen.to_dict())
+summaries = tok.batch_decode(out, skip_special_tokens=True)
+```
+
+---
+
+## 🔧 Default decoding (saved in `generation_config.json`)
+
+These are the defaults saved with the model (you can override at `generate()` time):
+
+```json
+{
+  "max_length": 142,
+  "min_length": 56,
+  "early_stopping": true,
+  "num_beams": 4,
+  "length_penalty": 2.0,
+  "no_repeat_ngram_size": 3,
+  "forced_bos_token_id": 0,
+  "forced_eos_token_id": 2
+}
+```
+
+---
+
+## 📊 Training details
+
+* **Base:** `sshleifer/distilbart-cnn-12-6` (Distilled BART)
+* **Data:** 19,992 CC-BY chemistry abstracts with TL;DR summaries
+* **Splits:** train=17,992 / val=999 / test=1,001
+* **Max lengths:** input 1024, target 128
+* **Optimizer:** AdamW, **lr=2e-5**
+* **Batching:** per-device train/eval batch size 4, **gradient\_accumulation\_steps=4**
+* **Epochs:** 5
+* **Precision:** fp16 (when CUDA available)
+* **Hardware:** single NVIDIA RTX 3090
+* **Seed:** 42
+* **Libraries:** 🤗 Transformers + Datasets, `evaluate` for ROUGE, NLTK for sentence splitting
+
+---
+
+## ✅ Intended use
+
+* TL;DR abstractive summaries for **chemistry** and adjacent domains (materials science, chemical engineering, environmental science).
+* **Semantic indexing**, **IR reranking**, and **knowledge graph** ingestion where concise method/result statements are helpful.
+
+### Limitations & risks
+
+* May **hallucinate** details not present in the abstract (typical for abstractive models).
+* Not a substitute for expert judgment; avoid using summaries as sole evidence for scientific claims.
+* Trained on CC-BY English abstracts; performance may degrade on other domains/languages.
+
+---
+
+## 📦 Files
+
+This repo should include:
+
+* `config.json`, `pytorch_model.bin` or `model.safetensors`
+* `tokenizer.json`, `tokenizer_config.json`, `special_tokens_map.json`, merges/vocab as applicable
+* `generation_config.json` (decoding defaults)
+
+---
+
+## 🔁 Reproducibility
+
+* Dataset: [`Bocklitz-Lab/lit2vec-tldr-bart-dataset`](https://huggingface.co/datasets/Bocklitz-Lab/lit2vec-tldr-bart-dataset)
+* Recommended preprocessing: truncate inputs at 1024 tokens; targets at 128.
+* ROUGE evaluation: `evaluate.load("rouge")`, NLTK sentence tokenization, `use_stemmer=True`.
+
+---
+
+## 📚 Citation
+
+If you use this model or dataset, please cite:
+
+```bibtex
+@software{lit2vec_tldr_bart_2025,
+  title   = {lit2vec-tldr-bart: DistilBART fine-tuned for chemistry TL;DR summarization},
+  author  = {Bocklitz Lab},
+  year    = {2025},
+  url     = {https://huggingface.co/Bocklitz-Lab/lit2vec-tldr-bart},
+  note    = {Model trained on CC-BY chemistry abstracts; dataset at Bocklitz-Lab/lit2vec-tldr-bart-dataset}
+}
+```
+
+Dataset:
+
+```bibtex
+@dataset{lit2vec_tldr_dataset_2025,
+  title   = {Lit2Vec TL;DR Chemistry Dataset},
+  author  = {Bocklitz Lab},
+  year    = {2025},
+  url     = {https://huggingface.co/datasets/Bocklitz-Lab/lit2vec-tldr-bart-dataset}
+}
+```
+
+---
+
+## 📝 License
+
+* **Model weights & code:** Apache-2.0
+* **Dataset:** CC BY 4.0 (attribution in per-record metadata)
+
+---
+
+## 🙌 Acknowledgements
+
+* Base model: DistilBART (`sshleifer/distilbart-cnn-12-6`)
+* Licensing and OA links curated from publisher/aggregator sources; dataset restricted to **CC-BY** content.