Instructions to use horsbug98/Part_2_XLM_Model_E1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use horsbug98/Part_2_XLM_Model_E1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="horsbug98/Part_2_XLM_Model_E1")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("horsbug98/Part_2_XLM_Model_E1") model = AutoModelForQuestionAnswering.from_pretrained("horsbug98/Part_2_XLM_Model_E1") - Notebooks
- Google Colab
- Kaggle
Upload trainer_state.json
Browse files- trainer_state.json +3 -3
trainer_state.json
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"step": 1072,
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"total_flos": 2520207218718720.0,
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"train_loss": 1.8482726438721615,
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"train_runtime":
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"train_samples_per_second":
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"train_steps_per_second": 1.
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}
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],
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"max_steps": 1072,
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"step": 1072,
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"total_flos": 2520207218718720.0,
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"train_loss": 1.8482726438721615,
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"train_runtime": 561.1453,
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"train_samples_per_second": 22.917,
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"train_steps_per_second": 1.91
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}
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],
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"max_steps": 1072,
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