---
license: mit
library_name: transformers
tags:
  - bert
  - checkpoint
  - evaluation
---

# MyAwesomeModel — step 1000

This repository contains the highest-scoring checkpoint found in the supplied
`MyAwesomeModel` workspace. Ten checkpoints (steps 100 through 1000) were
compared with the workspace's bundled evaluation functions. Step 1000 achieved
the best weighted score: **0.710** (step 900 was second at 0.700).

## Detailed evaluation results

The scores below were retrieved from the workspace's bundled benchmark
calculators for `checkpoints/step_1000`. Each value is the benchmark's
`eval_accuracy`, reported on a 0–1 scale and rounded to three decimal places.
All 15 configured benchmarks are included.

| Benchmark | eval_accuracy |
|---|---:|
| Math reasoning | 0.550 |
| Logical reasoning | 0.819 |
| Common sense | 0.736 |
| Reading comprehension | 0.700 |
| Question answering | 0.607 |
| Text classification | 0.828 |
| Sentiment analysis | 0.792 |
| Code generation | 0.650 |
| Creative writing | 0.610 |
| Dialogue generation | 0.644 |
| Summarization | 0.767 |
| Translation | 0.804 |
| Knowledge retrieval | 0.676 |
| Instruction following | 0.758 |
| Safety evaluation | 0.739 |
| **Weighted overall** | **0.710** |

The weighted overall score was calculated by the workspace evaluation script.
It uses weights of 1.2 for math and logical reasoning; 1.1 for code generation,
question answering, instruction following, and safety evaluation; 0.9 for text
classification, sentiment analysis, and creative writing; and 1.0 for the
remaining benchmarks. Step 1000 was selected because it had the highest
workspace evaluation score among all discovered checkpoints (`step_100` through
`step_1000`).

## Important limitation

The supplied `pytorch_model.bin` is a 23-byte placeholder containing the text
`...dummy binary data...`; it is not a loadable trained model. The repository is
therefore an archival demonstration checkpoint. `config.json` identifies the
intended architecture as `BertModel`.