# Load model directly
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("zASE123/my-awesome-model")
model = AutoModel.from_pretrained("zASE123/my-awesome-model", device_map="auto")Quick Links
MyAwesomeModel
MyAwesomeModel is a BERT-style model fine-tuned across many training steps. The best checkpoint (step 1000) achieves a weighted overall score of 0.71 across all 15 evaluation benchmarks.
Evaluation Results
Detailed benchmark scores for the best checkpoint (step_1000):
| # | Benchmark | Score |
|---|---|---|
| 1 | Math Reasoning | 0.550 |
| 2 | Logical Reasoning | 0.819 |
| 3 | Code Generation | 0.650 |
| 4 | Question Answering | 0.607 |
| 5 | Reading Comprehension | 0.700 |
| 6 | Common Sense | 0.736 |
| 7 | Text Classification | 0.828 |
| 8 | Sentiment Analysis | 0.792 |
| 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 |
Weighted Overall Score: 0.710
Model Details
- Model type: BERT (
BertModel) - Hidden size: 768
- Layers: 12
- Attention heads: 12
- Vocab size: 30522
- Total parameters: 109,482,240
License
MIT
- Downloads last month
- 53
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="zASE123/my-awesome-model")