Instructions to use sadafhjkj/MyAwesomeModel-step1000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sadafhjkj/MyAwesomeModel-step1000 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sadafhjkj/MyAwesomeModel-step1000")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("sadafhjkj/MyAwesomeModel-step1000") model = AutoModelForMaskedLM.from_pretrained("sadafhjkj/MyAwesomeModel-step1000", device_map="auto") - Notebooks
- Google Colab
- Kaggle
MyAwesomeModel
This repository contains the best checkpoint selected from the workspace evaluation sweep: checkpoints/step_1000.
Evaluation Results
The best checkpoint was selected by computing the weighted overall score across all 15 benchmark categories. The overall score for step_1000 was 0.710.
All 15 Benchmark Scores
| 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
config.json: Hugging Face Transformers-compatible model configuration.pytorch_model.bin: PyTorch checkpoint weights.
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