101 lines
2.7 KiB
Markdown
101 lines
2.7 KiB
Markdown
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---
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library_name: transformers
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license: apache-2.0
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language:
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- en
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base_model: distilbert/distilgpt2
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tags:
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- text-generation
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- causal-lm
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- arabic
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- fine-tuned
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- generated_from_trainer
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model-index:
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- name: my_awesome_eli5_clm-model
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results: []
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---
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# my_awesome_eli5_clm-model
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A Causal Language Model fine-tuned on Arabic text, based on [distilbert/distilgpt2](https://huggingface.co/distilbert/distilgpt2).
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Fine-tuned by **EhabSuliman** as part of an LLM course project.
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## Model Description
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- **Model type:** Causal Language Model (CLM)
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- **Base model:** [distilbert/distilgpt2](https://huggingface.co/distilbert/distilgpt2)
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- **Language:** English
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- **Fine-tuned by:** [EhabSuliman](https://huggingface.co/EhabSuliman)
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- **License:** Apache 2.0
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## Intended Uses & Limitations
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**Intended uses:**
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- Arabic text generation
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- Language modeling research
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- Educational/learning purposes
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**Limitations:**
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- Trained on a relatively small dataset
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- Loss is still relatively high (3.8027), meaning the model may generate inaccurate or repetitive text
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- Not recommended for production use without further fine-tuning
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## How to Use
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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tokenizer = AutoTokenizer.from_pretrained("EhabSuliman/my_awesome_eli5_clm-model")
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model = AutoModelForCausalLM.from_pretrained("EhabSuliman/my_awesome_eli5_clm-model")
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prompt = "Somatic hypermutation allows the immune system to"
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inputs = tokenizer(prompt, return_tensors="pt").input_ids
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outputs = model.generate(
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inputs,
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max_new_tokens=100,
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do_sample=True,
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top_k=50,
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top_p=0.95
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)
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print(tokenizer.batch_decode(outputs, skip_special_tokens=True))
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```
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## Training and Evaluation Data
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Fine-tuned on an Arabic text dataset using the ELI5 (Explain Like I'm 5) format.
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## Training Procedure
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### Training Hyperparameters
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The following hyperparameters were used during training:
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- **Learning rate:** 2e-05
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- **Train batch size:** 8
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- **Eval batch size:** 8
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- **Seed:** 42
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- **Optimizer:** AdamW (fused) with betas=(0.9, 0.999), epsilon=1e-08
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- **LR scheduler:** Linear
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- **Epochs:** 3
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### Training Results
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| Training Loss | Epoch | Step | Validation Loss |
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|:-------------:|:-----:|:----:|:---------------:|
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| 3.8556 | 1.0 | 1327 | 3.8101 |
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| 3.7851 | 2.0 | 2654 | 3.8035 |
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| 3.7514 | 3.0 | 3981 | 3.8027 |
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> The model shows steady improvement across epochs with validation loss decreasing from 3.8101 → 3.8027.
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### Framework Versions
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- Transformers 5.9.0
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- PyTorch 2.11.0+cu128
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- Datasets 4.0.0
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- Tokenizers 0.22.2
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## Author
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**Ehab Suliman** — Machine Learning Engineer
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🔗 [HuggingFace Profile](https://huggingface.co/EhabSuliman)
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