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README.md

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+---
+language: en
+license: mit
+tags:
+  - ci-cd
+  - github-actions
+  - bert
+  - lora
+  - peft
+  - xai
+  - explainable-ai
+  - text-classification
+  - software-engineering
+datasets:
+  - facebook/react
+metrics:
+  - f1
+  - accuracy
+model-index:
+  - name: AdaptCI-XAI
+    results:
+      - task:
+          type: text-classification
+        metrics:
+          - type: f1
+            value: 0.7124
+---
+
+# AdaptCI-XAI: CI Pipeline Failure Classifier
+
+**Research project:** IS 8101 — Sabaragamuwa University of Sri Lanka
+**Student:** Mary Angel Anton Premathas (20APC4548)
+**Paper:** AdaptCI-XAI: Explainable AI for CI Pipeline Failure Diagnosis using Transformer-Based Models on GitHub Actions
+
+## What this model does
+Classifies GitHub Actions CI/CD pipeline failure logs into 4 categories:
+| Label | Description |
+|-------|-------------|
+| `config_error` | Malformed YAML, outdated action versions |
+| `dependency_failure` | npm/pip install failures, missing packages |
+| `test_failure` | Unit/integration test failures, type errors |
+| `infrastructure` | Runner timeout, OOM, network errors |
+
+## How to use
+```python
+from transformers import pipeline
+clf = pipeline("text-classification", model="MaryAngel/AdaptCI-XAI")
+result = clf("npm ERR ENOENT no such file or directory node_modules/react")
+print(result)
+# [{'label': 'dependency_failure', 'score': 0.94}]
+```
+
+## Training details
+- **Base model:** bert-base-uncased
+- **Fine-tuning:** LoRA (r=8, alpha=16, target=query+value layers)
+- **Trainable parameters:** ~0.54% of BERT total
+- **Training data:** Real failed CI runs from facebook/react (GitHub API)
+- **Hardware:** Google Colab T4 GPU (free tier)
+- **Weighted F1:** 0.7124
+
+## Novelty
+1. First LoRA fine-tuning applied to CI/CD log classification
+2. First SHAP attribution on CI/CD failure predictions
+3. First expertise-aware (novice) adaptive explanation system for CI/CD
+4. Multi-source labelling: log text + workflow name + step name signals
+5. Fully reproducible on FREE hardware (Colab T4 + HF)
+
+## XAI
+Each prediction includes SHAP token attribution showing which log words drove the classification decision — making the black-box model transparent to developers.

config.json

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+{
+  "add_cross_attention": false,
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": null,
+  "classifier_dropout": null,
+  "dtype": "float32",
+  "eos_token_id": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "config_error",
+    "1": "dependency_failure",
+    "2": "test_failure",
+    "3": "infrastructure"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "is_decoder": false,
+  "label2id": {
+    "config_error": 0,
+    "dependency_failure": 1,
+    "infrastructure": 3,
+    "test_failure": 2
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "tie_word_embeddings": true,
+  "transformers_version": "5.0.0",
+  "type_vocab_size": 2,
+  "use_cache": false,
+  "vocab_size": 30522
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ba85ba322467f1e147c51100268f5f4a2b21911cd43dc67c964c5e5bad67768f
+size 437964776

tokenizer_config.json

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+{
+  "backend": "tokenizers",
+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "is_local": false,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}