|
|
--- |
|
|
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. 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) |
|
|
|
|
|
# 📘 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. |
|
|
|
|
|
# 📚 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 for ordinal sentiment analysis. 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 |
