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.
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.
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!
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.
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.
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)
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