Text Classification
Transformers
TensorBoard
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use AilingDAI/HM_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AilingDAI/HM_1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AilingDAI/HM_1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("AilingDAI/HM_1") model = AutoModelForSequenceClassification.from_pretrained("AilingDAI/HM_1") - Notebooks
- Google Colab
- Kaggle
| { | |
| "best_metric": 0.46741180953359257, | |
| "best_model_checkpoint": "distilbert-base-uncased-finetuned-cola/run-4/checkpoint-134", | |
| "epoch": 1.0, | |
| "eval_steps": 500, | |
| "global_step": 134, | |
| "is_hyper_param_search": true, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 1.0, | |
| "eval_loss": 0.46314120292663574, | |
| "eval_matthews_correlation": 0.46741180953359257, | |
| "eval_runtime": 0.803, | |
| "eval_samples_per_second": 1298.905, | |
| "eval_steps_per_second": 82.193, | |
| "step": 134 | |
| } | |
| ], | |
| "logging_steps": 500, | |
| "max_steps": 536, | |
| "num_input_tokens_seen": 0, | |
| "num_train_epochs": 4, | |
| "save_steps": 500, | |
| "total_flos": 0, | |
| "train_batch_size": 64, | |
| "trial_name": null, | |
| "trial_params": { | |
| "learning_rate": 3.0100334336501613e-05, | |
| "num_train_epochs": 4, | |
| "per_device_train_batch_size": 64, | |
| "seed": 15 | |
| } | |
| } | |