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Model: alakxender/dhivehi-gpt2-base
Source: Original Platform

README.md

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+---
+language:
+- dv
+base_model:
+- openai-community/gpt2
+datasets:
+- wikimedia/wikipedia
+---
+
+# GPT 2 DV base
+
+This is a GPT-2 model fine-tuned on Dhivehi language texts. The model was trained on a curated dataset of Dhivehi Wikipedia articles and can be used for text generation in the Dhivehi language.
+
+## Model Description
+
+- **Model Type:** GPT-2
+- **Language:** Dhivehi (ދިވެހި)
+- **Training Data:** Dhivehi Wikipedia articles
+- **Last Updated:** 2024-11-25
+
+## Performance Metrics
+
+
+Evaluation metrics on the test set:
+- Average Perplexity: 3.80
+- Perplexity Std: 2.23
+- Best Perplexity: 2.72
+
+## Usage Example
+
+```python
+from transformers import GPT2LMHeadModel, GPT2TokenizerFast
+
+# Load model and tokenizer
+model = GPT2LMHeadModel.from_pretrained("alakxender/dhivehi-gpt2-base")
+tokenizer = GPT2TokenizerFast.from_pretrained("alakxender/dhivehi-gpt2-base")
+
+# Prepare your prompt
+prompt = "ދިވެހިރާއްޖެއަކީ"
+inputs = tokenizer(prompt, return_tensors="pt")
+
+# Generate text
+outputs = model.generate(
+    **inputs,
+    max_length=200,
+    temperature=0.7,
+    top_p=0.9,
+    do_sample=True,
+    num_return_sequences=1
+)
+
+# Decode the generated text
+generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(generated_text)
+```
+
+## Training Details
+
+The model was trained using the following configuration:
+- Base model: GPT-2
+- Training type: Full fine-tuning
+- Mixed precision: FP16
+- Gradient checkpointing: Enabled
+
+### Hyperparameters:
+- Learning rate: 5e-5
+- Batch size: 32
+- Gradient accumulation steps: 2
+- Epochs: 3
+- Weight decay: 0.01
+- Warmup steps: 1000
+
+## Limitations
+
+- Primary training data is from Wikipedia, which may not cover all Dhivehi language contexts
+- May not perform well on specialized or technical content
+- Could reflect biases present in the training data
+- Not recommended for production use without thorough evaluation
+
+## Intended Uses
+
+This model is suitable for:
+- Dhivehi text generation
+- Research on Dhivehi NLP
+- Educational purposes
+- Experimental applications
+
+Not intended for:
+- Critical or production systems
+- Decision-making applications
+- Tasks requiring factual accuracy