AChierici84/sentiment-roberta-finetuned

Sentiment Roberta finetuned for company reputation analysis

This is a RoBERTa-base model trained on SamEval datasets and fine-tuned with customer tweets. The main task is sentiment analysis with the TweetEval benchmark. The original model can de found here This model is suitable for English. Labels:

• 0 > Negative;
• 1 > Neutral;
• 2 > Positive

Example Pipeline

from transformers import pipeline
sentiment_task = pipeline("sentiment-analysis", model=model_path, tokenizer=model_path)
sentiment_task("Delivery is late!")

Output format: Json

[{'label': 'Negative', 'score': 0.99836}]

Test application here.

Model Details

Model Description

This model is generated to evaluate costumer satisfaction and company reputation.

• Developed by: Anna Chierici
• Language(s) (NLP): English
• Finetuned from model [optional]: cardiffnlp/twitter-roberta-base-sentiment-latest

Training Details

Training Data

SemEval dataset and tweet sent to @AmazonHelp account

Training Procedure

Training Hyperparameters

The following training strategies were implemented:

• validated at the end of each era
• checkpoint saving
• initial learning rate equal to 2e-5
• training/validation batch size of 16
• number of epochs 3
• regularization with weight reduction
• evaluation of the best model at the end of training
• additional metrics accuracy and F1