Instructions to use Shaer-AI/ARBERT-base-submeter-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Shaer-AI/ARBERT-base-submeter-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Shaer-AI/ARBERT-base-submeter-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Shaer-AI/ARBERT-base-submeter-classifier") model = AutoModelForSequenceClassification.from_pretrained("Shaer-AI/ARBERT-base-submeter-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, step 17346, checkpoint
Browse files
last-checkpoint/model.safetensors
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last-checkpoint/optimizer.pt
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last-checkpoint/rng_state.pth
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last-checkpoint/scaler.pt
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last-checkpoint/scheduler.pt
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last-checkpoint/trainer_state.json
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"best_global_step": 17000,
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"best_metric": 0.9708837837837838,
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"best_model_checkpoint": "/kaggle/working/arbert_submeter_training/checkpoints/checkpoint-17000",
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"eval_steps": 500,
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"global_step":
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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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"should_evaluate": false,
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"should_save": true,
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"should_training_stop":
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"total_flos": 7.
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"train_batch_size": 128,
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"epoch": 3.0,
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"should_log": false,
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"should_training_stop": true
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"total_flos": 7.302829698913882e+16,
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