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
| { | |
| "best_metric": -9.722493171691895, | |
| "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": [ | |
| { | |
| "epoch": 1.0, | |
| "eval_loss": -9.722493171691895, | |
| "eval_runtime": 5.6439, | |
| "eval_samples_per_second": 17.895, | |
| "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 | |
| } | |