Instructions to use sdadafdaf4546/MyAwesomeModel-step1000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sdadafdaf4546/MyAwesomeModel-step1000 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="sdadafdaf4546/MyAwesomeModel-step1000")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("sdadafdaf4546/MyAwesomeModel-step1000") model = AutoModel.from_pretrained("sdadafdaf4546/MyAwesomeModel-step1000", device_map="auto") - Notebooks
- Google Colab
- Kaggle
MyAwesomeModel-step1000
This is the best checkpoint of MyAwesomeModel (step 1000), selected from 10 training checkpoints (step_100 through step_1000) based on comprehensive benchmark evaluation.
Overall Score
0.710 (weighted average across all 15 benchmarks)
Detailed Evaluation Results (15 Benchmarks)
| 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 |
Checkpoint Information
- Training step: 1000
- Model type: BERT (bert)
- Files: config.json, pytorch_model.bin
Usage
Load with transformers:
from transformers import AutoModel
model = AutoModel.from_pretrained("sdadafdaf4546/MyAwesomeModel-step1000")
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