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 # Warning: Pipeline type "question-answering" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("question-answering", model="horsbug98/Part_2_XLM_Model_E1")# pip install -U transformers accelerate # 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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload all_results.json
Browse files- all_results.json +11 -0
all_results.json
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{
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"epoch": 1.0,
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"eval_exact_match": 63.42710997442455,
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"eval_f1": 77.81944064327094,
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"eval_samples": 842,
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"train_loss": 1.8482726438721615,
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"train_runtime": 557.8506,
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"train_samples": 12860,
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"train_samples_per_second": 23.053,
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"train_steps_per_second": 1.922
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}
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