---
license: mit
library_name: transformers
tags:
  - bert
  - feature-extraction
  - text-classification
---

# MyAwesomeModel

This repository contains the checkpoint selected by the highest `eval_accuracy` in the supplied workspace: `checkpoints/step_1000`.

The workspace evaluation harness defines `eval_accuracy` through the text classification benchmark. `step_1000` has the highest value, `0.828`.

| Checkpoint | eval_accuracy |
|---|---:|
| checkpoints/step_100 | 0.517 |
| checkpoints/step_200 | 0.603 |
| checkpoints/step_300 | 0.667 |
| checkpoints/step_400 | 0.714 |
| checkpoints/step_500 | 0.750 |
| checkpoints/step_600 | 0.776 |
| checkpoints/step_700 | 0.795 |
| checkpoints/step_800 | 0.809 |
| checkpoints/step_900 | 0.820 |
| checkpoints/step_1000 | 0.828 |

## Detailed Evaluation Results

The following table contains the full evaluation results for the selected checkpoint, `checkpoints/step_1000`, across all 15 benchmarks found in the workspace.

| Benchmark | Score |
|---|---:|
| 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 |

The weighted overall score reported by the workspace evaluation harness is `0.710`.

## Artifact Note

The workspace provides `pytorch_model.bin` as a 23-byte placeholder file containing `...dummy binary data...`. It is preserved here alongside the supplied `config.json`; the checkpoint is not a loadable trained PyTorch model without the real weight file.