Instructions to use ASD123SAD21/MyAwesomeModel-TestRepository with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ASD123SAD21/MyAwesomeModel-TestRepository with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ASD123SAD21/MyAwesomeModel-TestRepository")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("ASD123SAD21/MyAwesomeModel-TestRepository") model = AutoModel.from_pretrained("ASD123SAD21/MyAwesomeModel-TestRepository", device_map="auto") - Notebooks
- Google Colab
- Kaggle
MyAwesomeModel
Best Checkpoint
The best checkpoint selected is step_1000, which achieves the highest overall weighted eval_accuracy across all 15 benchmark categories.
Overall weighted eval_accuracy (step_1000): 0.710
Evaluation Results โ Best Checkpoint (step_1000)
All scores below are computed from the benchmark scoring functions and rounded to three decimal places.
| # | Benchmark Category | eval_accuracy |
|---|---|---|
| 1 | math_reasoning | 0.550 |
| 2 | code_generation | 0.650 |
| 3 | text_classification | 0.828 |
| 4 | sentiment_analysis | 0.792 |
| 5 | question_answering | 0.607 |
| 6 | logical_reasoning | 0.819 |
| 7 | common_sense | 0.736 |
| 8 | reading_comprehension | 0.700 |
| 9 | dialogue_generation | 0.644 |
| 10 | summarization | 0.767 |
| 11 | translation | 0.804 |
| 12 | knowledge_retrieval | 0.676 |
| 13 | creative_writing | 0.610 |
| 14 | instruction_following | 0.758 |
| 15 | safety_evaluation | 0.739 |
Weighting Used for Overall Score
The overall weighted eval_accuracy combines the per-benchmark scores with the following weights (slightly emphasizing reasoning tasks):
| Benchmark | Weight |
|---|---|
| math_reasoning | 1.2 |
| logical_reasoning | 1.2 |
| code_generation | 1.1 |
| question_answering | 1.1 |
| instruction_following | 1.1 |
| safety_evaluation | 1.1 |
| reading_comprehension | 1.0 |
| common_sense | 1.0 |
| dialogue_generation | 1.0 |
| summarization | 1.0 |
| translation | 1.0 |
| knowledge_retrieval | 1.0 |
| text_classification | 0.9 |
| sentiment_analysis | 0.9 |
| creative_writing | 0.9 |
Per-Category eval_accuracy Trend Across Checkpoints
For reference, the overall weighted eval_accuracy for every checkpoint found in the workspace:
| Checkpoint | Overall 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 |
Architecture
model_type: bertarchitectures: ["BertModel"]
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