Instructions to use EmotiScan/amazon-comments-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EmotiScan/amazon-comments-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="EmotiScan/amazon-comments-bert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("EmotiScan/amazon-comments-bert") model = AutoModelForSequenceClassification.from_pretrained("EmotiScan/amazon-comments-bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 724 Bytes
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"best_model_checkpoint": "saved_bert_model/emoticon-model.bin/checkpoint-51",
"epoch": 1.0,
"eval_steps": 500,
"global_step": 51,
"is_hyper_param_search": false,
"is_local_process_zero": true,
"is_world_process_zero": true,
"log_history": [
{
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"eval_runtime": 5.6439,
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"eval_steps_per_second": 1.24,
"step": 51
}
],
"logging_steps": 500,
"max_steps": 51,
"num_input_tokens_seen": 0,
"num_train_epochs": 1,
"save_steps": 500,
"total_flos": 377815635419136.0,
"train_batch_size": 16,
"trial_name": null,
"trial_params": null
}
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