Instructions to use A11asda/MyAwesomeModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use A11asda/MyAwesomeModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="A11asda/MyAwesomeModel")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("A11asda/MyAwesomeModel") model = AutoModel.from_pretrained("A11asda/MyAwesomeModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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.
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Evaluation results
- eval_accuracy on Workspace evaluation suiteself-reported0.710