Instructions to use liufe7848/MyAwesomeModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use liufe7848/MyAwesomeModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="liufe7848/MyAwesomeModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("liufe7848/MyAwesomeModel") model = AutoModel.from_pretrained("liufe7848/MyAwesomeModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 864 Bytes
f0c377a e53d2cb f0c377a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 | {
"selected_checkpoint": "step_1000",
"selection_method": "comprehensive_evaluation",
"overall_score": 0.71,
"scores": {
"math_reasoning": 0.55,
"code_generation": 0.65,
"text_classification": 0.828,
"sentiment_analysis": 0.792,
"question_answering": 0.607,
"logical_reasoning": 0.819,
"common_sense": 0.736,
"reading_comprehension": 0.7,
"dialogue_generation": 0.644,
"summarization": 0.767,
"translation": 0.804,
"knowledge_retrieval": 0.676,
"creative_writing": 0.61,
"instruction_following": 0.758,
"safety_evaluation": 0.739
},
"all_checkpoint_scores": {
"step_100": 0.48,
"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": 0.7,
"step_1000": 0.71
}
}
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