Train an XMLRoBERTa model for Tone Analysis with Turkish Tweets 5 Label Tone Analysis Dataset.
XMLRoBERTa model should be able to correctly classify Turkish Tweets into one of the 5 classes given as : ["Kızgın", "Korku", "Mutlu", "Sürpriz", "Üzgün"]
Implementation Details
Train a transformers SequenceClassification model with pretrained Twitter XMLRoBERTa Base model.
Use the same train and test datasets we used to fine-tune gpt-3.5-turbo model.
Use tokenizer with max_length = 256 and max length padding.
Use the Hugging Face Transformers for training.
Design and Tasks
Develop a Python script that tokenizes tweet-tone data received in CSV format via Kaggle to the input tensors for the XMLRoBERTa model.
Use the same train and test datasets we used to fine-tune gpt-3.5-turbo model for a fair comparison.
Develop a Python script that creates a 5-label SequenceClassification model with pretrained Twitter XMLRoBERTa Base model.
Develop a training script using the Hugging Face Transformers library.
Determine the best parameters for training.
Acceptance Criteria
The model should be able to provide successful results for a 5-class classification task.
Task Description
Train an XMLRoBERTa model for Tone Analysis with Turkish Tweets 5 Label Tone Analysis Dataset. XMLRoBERTa model should be able to correctly classify Turkish Tweets into one of the 5 classes given as : ["Kızgın", "Korku", "Mutlu", "Sürpriz", "Üzgün"]
Implementation Details
Train a transformers SequenceClassification model with pretrained Twitter XMLRoBERTa Base model. Use the same train and test datasets we used to fine-tune gpt-3.5-turbo model. Use tokenizer with max_length = 256 and max length padding. Use the Hugging Face Transformers for training.
Design and Tasks
Develop a Python script that tokenizes tweet-tone data received in CSV format via Kaggle to the input tensors for the XMLRoBERTa model. Use the same train and test datasets we used to fine-tune gpt-3.5-turbo model for a fair comparison. Develop a Python script that creates a 5-label SequenceClassification model with pretrained Twitter XMLRoBERTa Base model. Develop a training script using the Hugging Face Transformers library. Determine the best parameters for training.
Acceptance Criteria
The model should be able to provide successful results for a 5-class classification task.