File size: 2,338 Bytes
83a3191 2133f67 83a3191 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 |
---
license: gpl-3.0
---
# reviewRoBERTa-large
This model is a fine-tuned version of [`roberta-large`](https://huggingface.co/FacebookAI/roberta-large) 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**: RoBERTa (Robustly Optimized BERT Approach)
- **Base Model**: `roberta-large`
- **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**: 8
- **Learning Rate**: 2e-5
- **Epochs**: 2
## Usage
### Load the model
```python
from transformers import RobertaTokenizer, RobertaForSequenceClassification
tokenizer = RobertaTokenizer.from_pretrained('quim-motger/reviewRoBERTa-large')
model = RobertaForSequenceClassification.from_pretrained('quim-motger/reviewRoBERTa-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.'}]
```
|