Update README.md

README.md

@@ -1,11 +1,59 @@
 ---
-language:
-- en
-tags:
-- question answering
+language: en
+tags: question answering
 license: apache-2.0
-datasets:
-- squad
-metrics:
-- squad
----
+datasets: squad
+metrics: squad
+---
+
+# BERT-base-cased for QA 
+**Language model:** bert-base-cased
+**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 = 2
+base_LM_model = "bert-base-cased"
+max_seq_len = 386
+learning_rate = 5e-5
+doc_stride=128
+max_query_length=64
+``` 
+## Performance
+Evaluated on the SQuAD v1.0 dev set.
+```
+"exact": 81.30,
+"f1": 88.58,
+```
+Evaluated on the battery device dataset.
+```
+"precision": 67.02,
+"recall": 80.15,
+```
+## Usage
+### In Transformers
+```python
+from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
+
+model_name = "batterydata/bert-base-cased-squad-v1"
+# a) Get predictions
+nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
+QA_input = {
+    'question': 'Why is model conversion important?',
+    'context': 'The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks.'
+}
+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