| --- |
| 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 |
| --- |
| |
| # Model description |
| 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). |
|
|
| 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) |
|
|
| ## 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 |
