base_model: unsloth/meta-llama-3.1-8b-instruct-bnb-4bit
library_name: peft
Fine-Tuned LLaMA 3.1 on Dependency Parsing
This model is a fine-tuned version of LLaMA 3.1 specifically designed to automate dependency parsing of simple sentences, categorizing words into their syntactic roles according to Universal Dependency Parsing tags.
Model Details
Model Description
The model has been fine-tuned to accurately parse simple sentences by classifying each word into its respective dependency category, such as nsubj, obj, and root, following the Universal Dependency framework. This fine-tuning enhances the LLaMA 3.1 model's ability to understand and analyze sentence structures, making it a valuable tool for linguistic analysis and natural language processing tasks.
- Developed by: Emanuel Pinasco
- Model type: NLP, Dependency Parsing
- Language(s) (NLP): English
Direct Use
The model can be used directly for syntactic analysis and linguistic research, where dependency parsing is required to understand sentence structures. It’s particularly suited for tasks involving simple sentence parsing.
Downstream Use [optional]
Ideal for integration into larger NLP systems that require detailed sentence parsing, such as grammar checking tools, machine translation systems, and educational software.
Out-of-Scope Use
The model is not designed for complex sentence structures, idiomatic expressions, or languages other than English. Misuse may involve attempts to apply it to tasks beyond simple dependency parsing, leading to inaccurate results.
Recommendations
Users (both direct and downstream) should be aware that the model's accuracy may decline with more complex or less conventional sentence structures. It's recommended to use this model in conjunction with other tools for more comprehensive linguistic analysis.
Training Details
Training Data
The model was trained on a curated dataset of simple English sentences annotated with Universal Dependency Parsing tags. The training data focused on ensuring high accuracy in syntactic role assignment.
Training Procedure
Training Hyperparameters
- Training regime: Mixed precision (fp16)
Evaluation
Testing Data, Factors & Metrics
Testing Data
The model was evaluated using a separate dataset of simple sentences annotated with Universal Dependency tags.
Factors
Evaluation focused on sentence simplicity, vocabulary diversity, and syntactic structure variations.
Metrics
Accuracy in word classification into dependency categories was the primary metric.
Summary
The fine-tuned model demonstrates high accuracy in dependency parsing of simple English sentences, making it a robust tool for basic syntactic analysis.
Model Card Authors
Emanuel Pinasco
Model Card Contact
Emanuel Pinasco
Framework versions
- PEFT 0.12.0