Update README.md

README.md

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-
 ---
 library_name: transformers
 tags:
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 - text-classification
 base_model: google/bigbird-roberta-base
 widget:
-- text: "I love AutoTrain"
+- text: I love AutoTrain
+license: mit
+language:
+- en
+metrics:
+- accuracy
+- bertscore
+pipeline_tag: text-classification
 ---
 
-# Model Trained Using AutoTrain
-
-- Problem type: Text Classification
-
-## Validation Metrics
-loss: 1.6760838031768799
-
-f1_macro: 0.27337963763262785
-
-f1_micro: 0.3093385214007782
-
-f1_weighted: 0.2814372687206247
+# 🛫 Big Bird Flight
+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% improved accuracy over its predecessor. Both models 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).
 
-precision_macro: 0.282150640368379
+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)
 
-precision_micro: 0.3093385214007782
+# 📘 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)]
 
-precision_weighted: 0.2911286647426314
+# 🧠 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).
 
-recall_macro: 0.30072312612988455
+# 🛠 Tokenizer
+- Based on SentencePiece Unigram model.
+- Uses a Metaspace tokenizer for subword splitting.
+- Max tokenised input length was set to 1024 tokens during preprocessing.
 
-recall_micro: 0.3093385214007782
+# 📌 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.
 
-recall_weighted: 0.3093385214007782
+# 📚 Citation
+If you're using this model in your research or applications, appreciate if you could buy me a coffe 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
 
-accuracy: 0.3093385214007782
+## 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