Update README.md

README.md

@@ -1,3 +1,63 @@
 ---
+language: en
+tags: question answering
 license: apache-2.0
+datasets: 
+- squad
+- batterydata/battery-device-data-qa
+metrics: squad
 ---
+
+# BatteryBERT-uncased for QA 
+**Language model:** batterybert-uncased
+**Language:** English  
+**Downstream-task:** Extractive QA  
+**Training data:** SQuAD v1
+**Eval data:** SQuAD v1
+**Code:**  See [example](https://github.com/ShuHuang/batterybert) 
+**Infrastructure**: 8x DGX A100
+## Hyperparameters
+```
+batch_size = 32
+n_epochs = 3
+base_LM_model = "batterybert-uncased"
+max_seq_len = 386
+learning_rate = 3e-5
+doc_stride=128
+max_query_length=64
+``` 
+## Performance
+Evaluated on the SQuAD v1.0 dev set.
+```
+"exact": 81.08,
+"f1": 88.41,
+```
+Evaluated on the battery device dataset.
+```
+"precision": 68.27,
+"recall": 80.88,
+```
+## Usage
+### In Transformers
+```python
+from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
+
+model_name = "batterydata/batterybert-uncased-squad-v1"
+# a) Get predictions
+nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
+QA_input = {
+    'question': 'What is the electrolyte?',
+    'context': 'The typical non-aqueous electrolyte for commercial Li-ion cells is a solution of LiPF6 in linear and cyclic carbonates.'
+}
+res = nlp(QA_input)
+# b) Load model & tokenizer
+model = AutoModelForQuestionAnswering.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+```
+## Authors
+Shu Huang: `sh2009 [at] cam.ac.uk`
+
+Jacqueline Cole: `jmc61 [at] cam.ac.uk`
+
+## Citation
+BatteryBERT: A Pre-trained Language Model for Battery Database Enhancement