Instructions to use frett/chinese_extract_pert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use frett/chinese_extract_pert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="frett/chinese_extract_pert", device_map="auto")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("frett/chinese_extract_pert") model = AutoModelForQuestionAnswering.from_pretrained("frett/chinese_extract_pert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 444 Bytes
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"epoch": 5.0,
"eval_exact_match": 80.89066134928548,
"eval_f1": 80.89066134928548,
"eval_runtime": 35.6788,
"eval_samples": 3941,
"eval_samples_per_second": 110.458,
"eval_steps_per_second": 27.635,
"total_flos": 3.6156938713344e+16,
"train_loss": 0.43488813713979024,
"train_runtime": 4328.4475,
"train_samples": 27675,
"train_samples_per_second": 31.969,
"train_steps_per_second": 7.992
} |