Instructions to use ChrisZeng/bart-base-detox with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ChrisZeng/bart-base-detox with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ChrisZeng/bart-base-detox") model = AutoModelForSeq2SeqLM.from_pretrained("ChrisZeng/bart-base-detox", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 0
Browse files- checkpoint-135/optimizer.pt +1 -1
- checkpoint-135/pytorch_model.bin +1 -1
- checkpoint-135/scheduler.pt +1 -1
- checkpoint-135/trainer_state.json +7 -7
- checkpoint-135/training_args.bin +1 -1
- pytorch_model.bin +1 -1
- training_args.bin +1 -1
checkpoint-135/optimizer.pt
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checkpoint-135/pytorch_model.bin
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checkpoint-135/scheduler.pt
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checkpoint-135/trainer_state.json
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{
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"best_metric": 0.
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"best_model_checkpoint": "outputs/bart-base-detox/checkpoint-135",
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"epoch": 0.9953917050691244,
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"global_step": 135,
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"log_history": [
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"learning_rate": 9e-
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"loss":
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"step": 135
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"best_metric": 0.8977488875389099,
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"best_model_checkpoint": "outputs/bart-base-detox/checkpoint-135",
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"epoch": 0.9953917050691244,
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"global_step": 135,
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"log_history": [
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{
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"epoch": 1.0,
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"learning_rate": 9e-07,
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"loss": 1.3574,
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"step": 135
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},
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{
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"epoch": 1.0,
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"eval_loss": 0.8977488875389099,
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"eval_runtime": 11.5957,
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"eval_samples_per_second": 267.341,
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"eval_steps_per_second": 33.461,
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"step": 135
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checkpoint-135/training_args.bin
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pytorch_model.bin
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training_args.bin
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