Update README.md

README.md

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 ---
-library_name: transformers
-tags: []
+license: apache-2.0
+datasets:
+- custom
+language:
+- en
+metrics:
+- f1
+- precision
+- recall
+base_model: batteryonline/batterybert-cased
+pipeline_tag: token-classification
+tags:
+- ner
+- electrocatalyst
+- materials-science
+- chemistry
+- battery
+- fuel-cell
+- durability
 ---
 
-# Model Card for Model ID
+# BatteryBERT Electrocatalyst NER v4
 
-<!-- Provide a quick summary of what the model is/does. -->
+Fine-tuned Named Entity Recognition model for extracting durability-related entities from electrocatalyst research literature.
 
+## Model Description
 
+This model is fine-tuned from [BatteryBERT](https://huggingface.co/batteryonline/batterybert-cased) for domain-specific NER in electrocatalyst and fuel cell research. It identifies key experimental parameters related to catalyst durability, degradation, and electrochemical performance.
 
-## Model Details
+### Supported Entity Types
 
-### Model Description
+| Entity | Description | Example |
+|--------|-------------|---------|
+| **MATERIAL** | Catalyst materials and compounds | IrO₂, Pt/C, NiFe-LDH |
+| **CONDITION** | Experimental conditions (voltage, temperature) | 1.6 V vs RHE, 80°C |
+| **METRIC** | Performance measurements | 10 mA/cm², 45 mV/dec |
+| **PROCESS** | Experimental techniques | electrodeposition, annealing, CV |
+| **ELECTROLYTE** | Electrolyte solutions | 0.5 M H₂SO₄, 1 M KOH |
+| **DURATION** | Time periods | 100 h, 5000 cycles |
 
-<!-- Provide a longer summary of what this model is. -->
+## Performance
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+### Overall Metrics
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+| Metric | Score |
+|--------|-------|
+| **F1** | **83.5%** |
+| Precision | 78.7% |
+| Recall | 88.9% |
 
-### Model Sources [optional]
+### Per-Entity Performance
 
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
+| Entity | Precision | Recall | F1 | Support |
+|--------|-----------|--------|-----|---------|
+| CONDITION | 0.75 | 0.91 | 0.82 | 175 |
+| DURATION | 0.79 | 0.89 | 0.84 | 133 |
+| ELECTROLYTE | 0.79 | 0.94 | 0.86 | 94 |
+| MATERIAL | 0.83 | 0.80 | 0.81 | 90 |
+| METRIC | 0.75 | 0.87 | 0.81 | 135 |
+| PROCESS | 0.86 | 0.90 | 0.88 | 151 |
 
 ## Training Details
 
 ### Training Data
 
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+- **Sentences**: 4,985
+- **Total Entities**: 8,381
+- **Source**: 245 open-access electrocatalyst research papers from MDPI, Nature Communications, Frontiers, and PubMed Central
+- **Focus**: Catalyst durability, degradation mechanisms, accelerated stress testing
 
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
+### Entity Distribution in Training Data
 
-## Technical Specifications [optional]
+| Entity | Count | Percentage |
+|--------|-------|------------|
+| CONDITION | 1,848 | 22.0% |
+| METRIC | 1,480 | 17.7% |
+| PROCESS | 1,405 | 16.8% |
+| DURATION | 1,193 | 14.2% |
+| ELECTROLYTE | 1,127 | 13.4% |
+| MATERIAL | 895 | 10.7% |
 
-### Model Architecture and Objective
+### Training Hyperparameters
 
-[More Information Needed]
+- **Base Model**: batteryonline/batterybert-cased
+- **Learning Rate**: 2e-5
+- **Batch Size**: 16
+- **Epochs**: 3
+- **Max Sequence Length**: 128
+- **Optimizer**: AdamW
+- **Training Regime**: fp16 mixed precision
 
-### Compute Infrastructure
+### Training Infrastructure
 
-[More Information Needed]
+- **Hardware**: NVIDIA T4 GPU (Google Colab)
+- **Training Time**: ~15 minutes
 
-#### Hardware
+## Usage
 
-[More Information Needed]
+```python
+from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline
 
-#### Software
+# Load model
+tokenizer = AutoTokenizer.from_pretrained("Dmjdxb/batterybert-electrocatalyst-ner-v4")
+model = AutoModelForTokenClassification.from_pretrained("Dmjdxb/batterybert-electrocatalyst-ner-v4")
 
-[More Information Needed]
+# Create pipeline
+ner_pipeline = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple")
 
-## Citation [optional]
+# Extract entities
+text = "IrO2 showed 10 mA/cm² at 1.6 V vs RHE in 0.5 M H2SO4 after 100 h."
+entities = ner_pipeline(text)
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+for entity in entities:
+    print(f"{entity['entity_group']}: {entity['word']} ({entity['score']:.2%})")
+```
 
-**BibTeX:**
+### Expected Output
 
-[More Information Needed]
+```
+MATERIAL: IrO2 (98%)
+METRIC: 10 mA/cm² (98%)
+METRIC: 1.6 V vs RHE (99%)
+ELECTROLYTE: 0.5 M H2SO4 (99%)
+DURATION: 100 h (87%)
+```
 
-**APA:**
+## Version History
 
-[More Information Needed]
+| Version | F1 Score | Training Data | Notes |
+|---------|----------|---------------|-------|
+| v2 | 41% | ~500 sentences | Initial fine-tuning |
+| v3 | 68% | 1,824 sentences | Improved training data |
+| **v4** | **83.5%** | 4,985 sentences | Expanded corpus, cleaned labels |
 
-## Glossary [optional]
+## Intended Use
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+This model is designed for:
+- Extracting experimental parameters from electrocatalyst research papers
+- Building structured databases of catalyst durability data
+- Automating literature review for materials science research
 
-[More Information Needed]
+## Limitations
 
-## More Information [optional]
+- Trained primarily on English-language academic papers
+- May not generalize well to patents or informal text
+- SUPPORT and FAILURE_MODE entities have limited training examples
 
-[More Information Needed]
+## Citation
 
-## Model Card Authors [optional]
+If you use this model, please cite:
 
-[More Information Needed]
+```bibtex
+@misc{batterybert-electrocatalyst-ner-v4,
+  author = {DurabilityGraph-AI},
+  title = {BatteryBERT Electrocatalyst NER v4},
+  year = {2025},
+  publisher = {Hugging Face},
+  url = {https://huggingface.co/Dmjdxb/batterybert-electrocatalyst-ner-v4}
+}
+```
 
-## Model Card Contact
+## Acknowledgments
 
-[More Information Needed]
+- Base model: [BatteryBERT](https://huggingface.co/batteryonline/batterybert-cased) by Battery Online
+- Training data sourced from open-access publications
+