Apollo 11 Transcript Unmasker

Character-level transformer trained to reconstruct masked Apollo 11 air-to-ground dialogue. Input: masked text (e.g. Ho**ton, *pollo 11.)
Output: reconstructed text in packed form (e.g. {Ho**ton, *pollo 11.|Houston, Apollo 11.})

Dataset

Trained on masked variants of the cleaned Apollo 11 transcript:

Architecture

Small causal character-level transformer (~3.7M parameters):

Setting Value
Vocab 256 (ASCII)
Model dim (d_model) 256
Attention heads 8
Transformer blocks 4
Max sequence length 2048
Positional encoding Learned absolute
Activation GELU
Normalization LayerNorm (pre-norm blocks)
Output Tied embedding projection β†’ next-character logits

Block layout:
x β†’ LayerNorm β†’ Multi-Head Self-Attention β†’ residual β†’ LayerNorm β†’ FeedForward (4Γ— expand) β†’ residual Attention is causal (each position only sees past tokens), so the model can be trained in parallel and used autoregressively at inference.

Training results

Best checkpoint after 8 epochs / 10,000 steps:

Metric Value
Train loss 0.69
Test loss 0.98
Test char accuracy (response tokens) ~72%

Training loss curve Loss is computed on response tokens only (after |). The curve shows train and test loss over training iterations.

Files

File Description
model.pt Best checkpoint (model state dict + training metrics)
config.json Architecture and training metadata
loss_curve.png Iteration vs train/test loss

Usage

This is a custom PyTorch model (not a Hugging Face transformers checkpoint). You need the model code from the training repo to load it.

import torch
from model import build_model
model = build_model()
state = torch.load("model.pt", map_location="cpu")
model.load_state_dict(state["model"])
model.eval()

Prompt format: wrap masked input as {masked| and generate until }.

Example:

Input: Hoton, *pollo 11. Prompt: {Ho**ton, *pollo 11.| Output: {Hoton, *pollo 11.|Houston, Apollo 11.}

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Dataset used to train MEBestawy/apollo-11-transcript-unmasker