Instructions to use bdpc/test_twowayloss_implementation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bdpc/test_twowayloss_implementation with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="bdpc/test_twowayloss_implementation")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("bdpc/test_twowayloss_implementation") model = AutoModelForSequenceClassification.from_pretrained("bdpc/test_twowayloss_implementation") - Notebooks
- Google Colab
- Kaggle
Saving best model of test_twowayloss_implementation to hub
Browse files- all_results.json +2 -2
- train_results.json +2 -2
- training_args.bin +1 -1
all_results.json
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{
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"epoch": 0.0,
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"train_loss": 8.502942276000976,
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"train_runtime":
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"train_samples_per_second": 0.
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"train_steps_per_second": 0.065
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}
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{
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"epoch": 0.0,
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"train_loss": 8.502942276000976,
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"train_runtime": 153.3253,
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"train_samples_per_second": 0.522,
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"train_steps_per_second": 0.065
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}
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train_results.json
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{
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"epoch": 0.0,
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"train_loss": 8.502942276000976,
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"train_runtime":
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"train_samples_per_second": 0.
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"train_steps_per_second": 0.065
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}
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{
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"epoch": 0.0,
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"train_loss": 8.502942276000976,
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"train_runtime": 153.3253,
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"train_samples_per_second": 0.522,
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"train_steps_per_second": 0.065
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}
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training_args.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size 4155
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version https://git-lfs.github.com/spec/v1
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oid sha256:757bd5925bae31ae22f37638e83124baca0f48d547ca3d9341caca7a5068f50c
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size 4155
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