| --- |
| library_name: transformers |
| tags: |
| - autotrain |
| - text-classification |
| base_model: google/bigbird-roberta-base |
| widget: |
| - text: I love AutoTrain |
| license: mit |
| language: |
| - en |
| metrics: |
| - accuracy |
| - bertscore |
| pipeline_tag: text-classification |
| --- |
| |
| # 🛫 Big Bird Flight |
| Big Bird Flight is a fine-tuned version of Google’s BigBird model, optimised for long-text sentiment analysis in the context of airline passenger experiences. It was trained on 2,598 flight review texts, each annotated with a 10-point ordinal sentiment rating ranging from 1 (extremely negative) to 10 (extremely positive). |
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| Big Bird Flight captures nuanced emotional gradients in text, offering richer sentiment analysis than conventional binary classification (e.g., positive vs. negative). This makes it particularly useful for applications requiring fine-grained sentiment understanding from lengthy or detailed customer feedback. |
| - Use case: text classification |
| - Sentiment class: 1 (extremely negative) to 10 (extremely positive) |
|
|
| # 📘 Model details |
| - Base model: google/bigbird-roberta-base |
| - Architecture: BigBirdForSequenceClassification |
| - Hidden size: 768 |
| - Layers: 12 transformer blocks |
| - Attention type: block-sparse |
| - Max sequence length: 4096 tokens |
| - Number of classes: 10 [ratings from 1 to 10 (extremely negative/extremely positive)] |
|
|
| # 🧠 Training Summary |
| - Dataset: 2,598 airline passenger reviews. |
| - Labels: ordinal scale from 1 (extremely negative) to 10 (extremely positive). |
| - Loss function: cross-entropy (classification setup). |
|
|
| # 🛠 Tokenizer |
| - Based on SentencePiece Unigram model. |
| - Uses a Metaspace tokenizer for subword splitting. |
| - Max tokenized input length is set to 128 tokens during preprocessing. |
|
|
| # 📌 Use cases |
| - Analyse detailed customer reviews from air travel. |
| - Replace coarse binary sentiment models with ordinal sentiment scales. |
| - Experiment with ordinal regression techniques in NLP. |
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|
| # 📚 Citation |
| If you use this model in your research or applications, appreciate if you could cite as follow. |
| Mat Roni, S. (2025). Big Bird Flight: Fine-tuned BigBird for Ordinal Sentiment Analysis of Airline Reviews. Hugging Face. https://huggingface.co/pvaluedotone/bigbird-flight |
|
|
| ## Validation metrics |
| The validation metrics reflects the inherent complexity in the fine granularity of the 10-point scale. |
| - loss: 1.7985 |
| - f1_macro: 0.2275 |
| - f1_micro: 0.2665 |
| - f1_weighted: 0.2347 |
| - precision_macro: 0.2676 |
| - precision_micro: 0.2665 |
| - precision_weighted: 0.2777 |
| - recall_macro: 0.2595 |
| - recall_micro: 0.2665 |
| - recall_weighted: 0.2665 |
| - accuracy: 0.2665 |
