Update README

README.md

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----
-license: gpl-3.0
----
+---
+license: gpl-3.0
+---
+
+# reviewBERT-large
+
+This model is a fine-tuned version of [`bert-large-uncased`](https://huggingface.co/google-bert/bert-large-uncased) on a large dataset 
+of mobile app reviews. The model is designed to understand and process text from mobile app reviews, providing enhanced performance 
+for tasks such as feature extraction, sentiment analysis and review summarization from app reviews.
+
+## Model Details
+
+- **Model Architecture**: BERT (Bidirectional Encoder Representations from Transformers)
+- **Base Model**: `bert-large-uncased`
+- **Pre-training Extension**: Mobile app reviews dataset
+- **Language**: English
+
+## Dataset
+
+The extended pre-training was performed using a diverse dataset of mobile app reviews collected from various app stores. 
+The dataset includes reviews of different lengths, sentiments, and topics, providing a robust foundation for understanding 
+the nuances of mobile app user feedback.
+
+## Training Procedure
+
+The model was fine-tuned using the following parameters:
+
+- **Batch Size**: 16
+- **Learning Rate**: 2e-5
+- **Epochs**: 2
+
+## Usage
+
+### Load the model
+
+```python
+from transformers import BertTokenizer, BertForSequenceClassification
+
+tokenizer = BertTokenizer.from_pretrained('quim-motger/reviewBERT-large')
+model = BertForSequenceClassification.from_pretrained('quim-motger/reviewBERT-large')
+```
+
+### Example: Sentiment Analysis
+
+```python
+from transformers import pipeline
+
+nlp = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer)
+
+review = "This app is fantastic! I love the user-friendly interface and features."
+result = nlp(review)
+
+print(result)
+# Output: [{'label': 'POSITIVE', 'score': 0.98}]
+```
+
+### Example: Review Summarization
+
+```python
+from transformers import pipeline
+
+summarizer = pipeline('summarization', model=model, tokenizer=tokenizer)
+
+long_review = "I have been using this app for a while and it has significantly improved my productivity.
+The range of features is excellent, and the user interface is intuitive. However, there are occasional
+bugs that need fixing."
+summary = summarizer(long_review, max_length=50, min_length=25, do_sample=False)
+
+print(summary)
+# Output: [{'summary_text': 'The app has significantly improved my productivity with its excellent features and intuitive user interface. However, occasional bugs need fixing.'}]
+```