ALP1.0

Andel Language Model (bert-base-uncased Fine-Tuned)

Model Description

This model is a fine-tuned version of bert-base-uncased, designed for analyzing and processing the fictional Andel language. The fine-tuning focuses on token classification tasks, such as part-of-speech tagging and Named Entity Recognition (NER), using Andel-specific linguistic data.

The Andel language is part of a fictional world, Jahre, and this model serves as a tool for both linguistic exploration and interactive user engagement.

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Intended Use and Limitations

Use Cases:

• Analyzing Andel sentences for parts of speech.
• Identifying named entities or unique grammatical structures in Andel text.
• Supporting interactive learning and creative writing in the fictional language.

Limitations:

• The model is fine-tuned on a fictional language and may not generalize well to other linguistic contexts.
• As a fictional language, Andel's dataset is relatively small, which might result in occasional inaccuracies.
• Not suitable for real-world applications outside the context of the Andel language.

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Training Data

The model was fine-tuned on a custom dataset of Andel sentences. The data includes:

• Annotated sentences for parts-of-speech tagging.
• Examples of Andel grammar and vocabulary derived from Pre-Andellic roots.
• A limited corpus of fictional texts written in Andel.

If you are curious about contributing to the dataset or exploring the linguistic rules of Andel, feel free to reach out!

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Training Process

The fine-tuning process utilized:

• Base Model: bert-base-uncased
• Framework: Hugging Face's Transformers library.
• Dataset Size: Approximately XX sentences with POS annotations.
• Hyperparameters:
  • Learning Rate: 2e-5
  • Batch Size: 16
  • Epochs: 5

The model was trained on GPU resources, with parameter-efficient optimization techniques to accommodate the limited dataset size.

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Performance

Metrics:

• POS Tagging Accuracy: XX%
• F1-Score (NER): XX%
• Loss (Validation): XX

These results are based on a held-out test set of Andel sentences and reflect the model's performance within the Andel-specific task domain.

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How to Use

To use the model, you can load it via the Hugging Face Transformers library:

from transformers import AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("your-username/andel-language-model")
model = AutoModelForTokenClassification.from_pretrained("your-username/andel-language-model")

inputs = tokenizer("h’dœnšubh is dark", return_tensors="pt")
outputs = model(**inputs)