Instructions to use dyllanesl/ASL_Classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dyllanesl/ASL_Classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="dyllanesl/ASL_Classifier") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("dyllanesl/ASL_Classifier") model = AutoModelForImageClassification.from_pretrained("dyllanesl/ASL_Classifier") - Notebooks
- Google Colab
- Kaggle
Upload trainer_state.json
Browse files- trainer_state.json +32 -0
trainer_state.json
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{
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"best_metric": null,
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"best_model_checkpoint": null,
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"epoch": 3.0,
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"eval_steps": 500,
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"global_step": 6,
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"is_hyper_param_search": false,
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"is_local_process_zero": true,
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"is_world_process_zero": true,
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"log_history": [],
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"logging_steps": 10,
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"max_steps": 6,
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"num_input_tokens_seen": 0,
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"num_train_epochs": 3,
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"save_steps": 500,
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"stateful_callbacks": {
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"TrainerControl": {
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"args": {
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"should_epoch_stop": false,
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"should_evaluate": false,
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"should_log": false,
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"should_save": true,
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"should_training_stop": true
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},
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"attributes": {}
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}
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},
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"total_flos": 4417914356416512.0,
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"train_batch_size": 16,
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"trial_name": null,
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"trial_params": null
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}
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