Create README.md

README.md

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+---
+license: openrail
+datasets:
+- imdb
+language:
+- en
+metrics:
+- accuracy
+library_name: transformers
+pipeline_tag: text-classification
+---
+
+## Model description
+This model is a fine-tuned version of the [bert-base-uncased](https://huggingface.co/transformers/model_doc/bert.html) model to classify the sentiment of movie reviews into one of two
+categories: negative(label 0), positive(label 1).
+
+## How to use
+
+You can use the model with the following code.
+
+```python
+from transformers import BertForSequenceClassification, BertTokenizer, TextClassificationPipeline
+model_path = "JiaqiLee/imdb-finetuned-bert-base-uncased"
+tokenizer = BertTokenizer.from_pretrained(model_path)
+model = BertForSequenceClassification.from_pretrained(model_path, num_labels=2)
+pipeline =  TextClassificationPipeline(model=model, tokenizer=tokenizer)
+print(pipeline("The movie depicted well the psychological battles that Harry Vardon fought within himself, from his childhood trauma of being evicted to his own inability to break that glass ceiling that prevents him from being accepted as an equal in English golf society."))
+```
+
+## Training data
+The training data comes HuggingFace [IMDB dataset](https://huggingface.co/datasets/imdb). We use 90% of the `train.csv` data to train the model and the remaining 10% for evaluation.
+
+## Evaluation results
+
+The model achieves 0.91 classification accuracy in IMDB test dataset.