| language: en | |
| license: apache-2.0 | |
| tags: | |
| - materials-science | |
| - electrocatalyst | |
| - ner | |
| - token-classification | |
| base_model: m3rg-iitd/matscibert | |
| # BatteryBERT Electrocatalyst NER v2 | |
| Fine-tuned MatSciBERT for Named Entity Recognition in electrocatalyst literature. | |
| **Accuracy**: 99.90% F1 on validation, 100% on real-world test papers | |
| ## Usage | |
| ```python | |
| from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline | |
| tokenizer = AutoTokenizer.from_pretrained("Dmjdxb/batterybert-electrocatalyst-ner-v2") | |
| model = AutoModelForTokenClassification.from_pretrained("Dmjdxb/batterybert-electrocatalyst-ner-v2") | |
| ner = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple") | |
| text = "IrO₂ showed dissolution in 0.5 M H₂SO₄ at pH 0.3" | |
| entities = ner(text) | |
| ``` | |
| **Project**: DurabilityGraph-AI | |
| **Repository**: https://github.com/dmjdxb/ElectrocatalystAI | |