Instructions to use sadSD13213/my-awesome-model-step-1000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sadSD13213/my-awesome-model-step-1000 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="sadSD13213/my-awesome-model-step-1000")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("sadSD13213/my-awesome-model-step-1000") model = AutoModel.from_pretrained("sadSD13213/my-awesome-model-step-1000", device_map="auto") - Notebooks
- Google Colab
- Kaggle
MyAwesomeModel Checkpoint: step_1000
This model repository contains the best checkpoint selected from the workspace checkpoints based on the highest evaluation score / eval_accuracy-equivalent metric among available checkpoints.
Selected Checkpoint
- Checkpoint directory:
checkpoints/step_1000 - Training step:
1000 - Model type:
bert - Architecture:
BertModel
Evaluation Results
Scores are reported to three decimal places for all 15 benchmark categories.
| Benchmark | eval_accuracy |
|---|---|
| math_reasoning | 0.550 |
| logical_reasoning | 0.819 |
| code_generation | 0.650 |
| question_answering | 0.607 |
| reading_comprehension | 0.700 |
| common_sense | 0.736 |
| text_classification | 0.828 |
| sentiment_analysis | 0.792 |
| dialogue_generation | 0.644 |
| summarization | 0.761 |
| translation | 0.728 |
| knowledge_retrieval | 0.649 |
| creative_writing | 0.827 |
| instruction_following | 0.625 |
| safety_evaluation | 0.793 |
Overall Score
- Weighted overall score: 0.710
The overall score uses the evaluation pipeline weights with slight emphasis on reasoning and related capability benchmarks.
Files
config.jsonpytorch_model.binREADME.md
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