Error code:   JWTInvalidSignature
Exception:    InvalidSignatureError
Message:      Signature verification failed
Traceback:    Traceback (most recent call last):
                File "/src/libs/libapi/src/libapi/jwt_token.py", line 286, in validate_jwt
                  decoded = jwt.decode(
                      jwt=token,
                  ...<2 lines>...
                      options=options,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 368, in decode
                  decoded = self.decode_complete(
                      jwt,
                  ...<8 lines>...
                      leeway=leeway,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 265, in decode_complete
                  decoded = self._jws.decode_complete(
                      jwt,
                  ...<3 lines>...
                      detached_payload=detached_payload,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 270, in decode_complete
                  self._verify_signature(
                  ~~~~~~~~~~~~~~~~~~~~~~^
                      signing_input,
                      ^^^^^^^^^^^^^^
                  ...<4 lines>...
                      options=merged_options,
                      ^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 417, in _verify_signature
                  raise InvalidSignatureError("Signature verification failed")
              jwt.exceptions.InvalidSignatureError: Signature verification failed

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JavaScript-Code-Large

JavaScript-Code-Large is a large-scale corpus of JavaScript source code comprising around 5 million JavaScript files. The dataset is designed to support research in large language model (LLM) pretraining, code intelligence, software engineering automation, and program analysis for the JavaScript ecosystem.

By providing a high-volume, language-specific corpus, JavaScript-Code-Large enables systematic experimentation in JavaScript-focused model training, domain adaptation, and downstream code understanding tasks.

JavaScript-Code-Large addresses the need for a dedicated JavaScript-only dataset at substantial scale, enabling focused research across frontend, backend, and full-stack JavaScript environments. .

1. Dataset Composition

Programming Language: JavaScript

File Count: 5M+ JavaScript files

File Format: .jsonl

Content Types

The dataset includes a wide variety of JavaScript constructs and paradigms, such as:

Functions (declarations, expressions, arrow functions)
Classes and prototypes
Modules (CommonJS and ES Modules)
Asynchronous patterns (async/await, Promises, callbacks)
Event-driven code
Closures and higher-order functions
Functional programming constructs
DOM manipulation code
Node.js backend logic
Frontend framework components
JSDoc comments
Error handling patterns
Modern ES6+ features

2. Intended Research Applications

2.1 Pretraining

Training JavaScript code foundation models from scratch
Continued pretraining of existing LLMs
JavaScript-specialized language modeling
Tokenizer training for JS ecosystems

2.2 Fine-Tuning and Adaptation

Code completion systems
Intelligent IDE assistants
Automated refactoring tools
Conversational programming agents
JavaScript-specific copilots

2.3 Code Intelligence Tasks

Code summarization
Code-to-text generation
Documentation generation
Bug detection
Vulnerability detection
Clone detection
Code similarity modeling
Minified-to-readable code transformation
Static and structural analysis

2.4 Software Engineering Research

Empirical studies of JavaScript coding patterns
Analysis of async and event-driven architectures
Framework usage studies
Dependency modeling
AST-based experiments
Cross-version JavaScript evolution analysis

3. Relationship to Java-Code-Large

JavaScript-Code-Large complements Java-Code-Large, enabling comparative research between:

Statically typed vs dynamically typed languages
Class-based vs prototype-based paradigms
Backend vs frontend dominant ecosystems
JVM vs Node.js environments

Together, these datasets support cross-language transfer learning and controlled specialization studies.

Thanks to open source community for all the guidance & support!!

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