---
license: mit
library_name: transformers
pipeline_tag: feature-extraction
tags:
- bert
- checkpoint-selection
model-index:
- name: MyAwesomeModel
  results:
  - task:
      type: feature-extraction
    dataset:
      name: Workspace evaluation suite
      type: custom
    metrics:
    - name: eval_accuracy
      type: accuracy
      value: 0.710
---
# MyAwesomeModel

This repository contains the best checkpoint found in the training workspace.
It was selected by comparing `eval_accuracy` across every discovered checkpoint.

- **Selected checkpoint:** `step_1000`
- **eval_accuracy:** **0.710**
- **Architecture declared by the checkpoint:** `BertModel`

## Checkpoint comparison

| Checkpoint | eval_accuracy |
|---|---:|
| step_100 | 0.480 |
| step_200 | 0.535 |
| step_300 | 0.576 |
| step_400 | 0.608 |
| step_500 | 0.635 |
| step_600 | 0.656 |
| step_700 | 0.674 |
| step_800 | 0.689 |
| step_900 | 0.700 |
| step_1000 | 0.710 |

## Selected-checkpoint benchmark results

| 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 |

## Files

The selected checkpoint's `config.json` and `pytorch_model.bin` are stored at the repository root for compatibility with `transformers` loading conventions.

> Note: only checkpoint artifacts present in the workspace were published. No tokenizer files were available in the checkpoint directory.