Instructions to use sms1097/relevant_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sms1097/relevant_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sms1097/relevant_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sms1097/relevant_model") model = AutoModelForSequenceClassification.from_pretrained("sms1097/relevant_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "best_metric": 0.764684941260235, | |
| "best_model_checkpoint": "relevant_model/run-0/checkpoint-1113", | |
| "epoch": 1.0, | |
| "eval_steps": 500, | |
| "global_step": 1113, | |
| "is_hyper_param_search": true, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 0.45, | |
| "learning_rate": 8.028932935250765e-07, | |
| "loss": 0.5031, | |
| "step": 500 | |
| }, | |
| { | |
| "epoch": 0.9, | |
| "learning_rate": 1.480047996220777e-07, | |
| "loss": 0.42, | |
| "step": 1000 | |
| }, | |
| { | |
| "epoch": 1.0, | |
| "eval_f1": 0.764684941260235, | |
| "eval_loss": 0.40596920251846313, | |
| "eval_runtime": 7.7707, | |
| "eval_samples_per_second": 509.094, | |
| "eval_steps_per_second": 7.979, | |
| "step": 1113 | |
| } | |
| ], | |
| "logging_steps": 500, | |
| "max_steps": 1113, | |
| "num_input_tokens_seen": 0, | |
| "num_train_epochs": 1, | |
| "save_steps": 500, | |
| "total_flos": 7085134726089984.0, | |
| "train_batch_size": 64, | |
| "trial_name": null, | |
| "trial_params": { | |
| "learning_rate": 1.4577817874280751e-06, | |
| "num_train_epochs": 1, | |
| "per_device_train_batch_size": 64, | |
| "seed": 36 | |
| } | |
| } | |