Instructions to use dsad1dsa21/my-awesome-model-step-1000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dsad1dsa21/my-awesome-model-step-1000 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="dsad1dsa21/my-awesome-model-step-1000")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("dsad1dsa21/my-awesome-model-step-1000") model = AutoModel.from_pretrained("dsad1dsa21/my-awesome-model-step-1000", device_map="auto") - Notebooks
- Google Colab
- Kaggle
MyAwesomeModel Step 1000
This repository contains checkpoint step_1000, selected because it has the highest eval_accuracy among the workspace checkpoints.
- Checkpoint:
checkpoints/step_1000 - Selection metric:
eval_accuracy - Eval accuracy: 0.828
Evaluation Results
Scores below are reported for training step 1000 using the workspace evaluation artifacts. All scores are rounded to three decimal places.
| Benchmark | Score |
|---|---|
| math_reasoning | 0.550 |
| code_generation | 0.650 |
| text_classification | 0.828 |
| sentiment_analysis | 0.792 |
| question_answering | 0.607 |
| logical_reasoning | 0.819 |
| common_sense | 0.736 |
| reading_comprehension | 0.700 |
| dialogue_generation | 0.644 |
| summarization | 0.767 |
| translation | 0.804 |
| knowledge_retrieval | 0.676 |
| creative_writing | 0.610 |
| instruction_following | 0.758 |
| safety_evaluation | 0.739 |
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