README.md

---
library_name: transformers
tags:
- autotrain
- text-classification
base_model: google/bigbird-roberta-base
widget:
- text: Let me fly
license: mit
language:
- en
metrics:
- accuracy
pipeline_tag: text-classification
---
# 🛫 Big Bird Flight 2
Big Bird Flight 2 is a fine-tuned version of Google’s BigBird model, optimised for long-text sentiment analysis. Big Bird Flight 2 is an improved version of Big Bird Flight 1. The model records a 16% improvement in accuracy over its predecessor. Both models were 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).

Just like its predecessor, Big Bird 2 captures 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 tokenised input length was set to 1024 tokens during preprocessing.

# 📌 Use cases
- Analyse detailed customer reviews of flight experience.
- Replace coarse binary sentiment models with ordinal sentiment scales.
- Experiment with ordinal regression techniques in NLP.

# 📚 Citation
If you're using this model in your research or applications, appreciate if you could buy me a coffee through this citation.

Mat Roni, S. (2025). Big Bird Flight 2 for ordinal sentiment analysis [software]. Hugging Face. https://huggingface.co/pvaluedotone/bigbird-flight-2 DOI: https://doi.org/10.57967/hf/5780

## Validation metrics
- loss: 1.6761
- f1_macro: 0.2734
- f1_micro: 0.3093
- f1_weighted: 0.2814
- precision_macro: 0.2822
- precision_micro: 0.3093
- precision_weighted: 0.2911
- recall_macro: 0.3007
- recall_micro: 0.3093
- recall_weighted: 0.3093
- accuracy: 0.3093