| | --- |
| | tags: |
| | - generated_from_trainer |
| | model-index: |
| | - name: lm-gpt2-timemachine |
| | results: [] |
| | license: apache-2.0 |
| | datasets: |
| | - SOULAMA/timemachine-dataset-preprocessed |
| | language: |
| | - en |
| | metrics: |
| | - perplexity |
| | base_model: |
| | - openai-community/gpt2 |
| | pipeline_tag: text-generation |
| | --- |
| | |
| | <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| | should probably proofread and complete it, then remove this comment. --> |
| |
|
| | # lm-gpt2-timemachine |
| |
|
| | This model is a **causal language model** trained from scratch on the novel |
| | **_The Time Machine_ by H. G. Wells**. |
| |
|
| | The objective of this project is educational: to understand the full pipeline of |
| | **training a GPT-style language model from raw text**, including preprocessing, |
| | tokenization, training, and text generation. |
| |
|
| | --- |
| |
|
| | ## Model description |
| |
|
| | - **Architecture**: GPT-2–style causal language model |
| | - **Training type**: From scratch (no pretrained weights) |
| | - **Language**: English |
| | - **Tokenizer**: Byte-level BPE |
| | - **Context length**: 128 tokens |
| | - **Task**: Causal language modeling (next-token prediction) |
| |
|
| | The model learns to predict the next token given a sequence of previous tokens, |
| | and can be used to generate text in the style of *The Time Machine*. |
| |
|
| | --- |
| |
|
| | ## Intended uses & limitations |
| |
|
| | ### Intended uses |
| | - Educational purposes |
| | - Learning how causal language models work |
| | - Small-scale text generation experiments |
| | - Understanding Hugging Face training workflows |
| |
|
| | ### Limitations |
| | - Trained on a **very small corpus** (single novel) |
| | - Not suitable for general-purpose text generation |
| | - May produce repetitive or incoherent text |
| | - Not optimized for factual correctness |
| |
|
| | --- |
| |
|
| | ## Training and evaluation data |
| |
|
| | - **Dataset**: *The Time Machine* by H. G. Wells |
| | - **Source**: Public domain text |
| | - **Preprocessing**: |
| | - Text normalization |
| | - Tokenization with a byte-level tokenizer |
| | - Concatenation of text and splitting into fixed-length blocks (128 tokens) |
| |
|
| | The same dataset was split into training, validation, and test subsets. |
| |
|
| | --- |
| |
|
| | ## Training procedure |
| |
|
| | - **Objective**: Causal Language Modeling |
| | - **Loss function**: Cross-entropy loss |
| | - **Optimizer**: AdamW |
| | - **Batching**: Fixed-length token blocks |
| | - **Evaluation**: Perplexity on validation data |
| |
|
| | The model was trained using the Hugging Face `Trainer` API. |
| |
|
| | --- |
| |
|
| | ## Example generation |
| |
|
| | ```text |
| | The Time Traveller (for so it will be convenient to speak of him) was a curious man, |
| | of no less intellectual character than... |
| | |
| | |
| | ### Training hyperparameters |
| | |
| | The following hyperparameters were used during training: |
| | - learning_rate: 5e-05 |
| | - train_batch_size: 8 |
| | - eval_batch_size: 8 |
| | - seed: 42 |
| | - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| | - lr_scheduler_type: linear |
| | - num_epochs: 3 |
| | - mixed_precision_training: Native AMP |
| | |
| | ### Training results |
| | |
| | - 'eval_loss': 2.851572036743164 |
| | - 'eval_perplexity': 17.314979553222656 |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.57.6 |
| | - Pytorch 2.6.0+cu126 |
| | - Datasets 3.6.0 |
| | - Tokenizers 0.22.1 |
