Instructions to use asdafee34/MyAwesomeModel-TestRepository with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use asdafee34/MyAwesomeModel-TestRepository with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="asdafee34/MyAwesomeModel-TestRepository")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("asdafee34/MyAwesomeModel-TestRepository") model = AutoModel.from_pretrained("asdafee34/MyAwesomeModel-TestRepository", device_map="auto") - Notebooks
- Google Colab
- Kaggle
MyAwesomeModel-TestRepository
This repository contains the best checkpoint selected from the workspace based on the highest eval_accuracy among available checkpoints.
Selected Checkpoint
- Checkpoint:
step_1000 - Selection metric:
text_classification/eval_accuracy - Selected
eval_accuracy:0.828
Evaluation Results
All benchmark scores below are reported to three decimal places for the selected checkpoint.
| 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 |
Notes
- The selected checkpoint was chosen because it has the highest
eval_accuracyscore among the checkpoints found in the workspace. - Scores are formatted to three decimal places as requested.
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