Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use xshubhamx/bert-base-cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xshubhamx/bert-base-cased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="xshubhamx/bert-base-cased")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("xshubhamx/bert-base-cased") model = AutoModelForSequenceClassification.from_pretrained("xshubhamx/bert-base-cased", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "best_metric": 0.660482894909786, | |
| "best_model_checkpoint": "bert-base-cased/checkpoint-1286", | |
| "epoch": 2.0, | |
| "eval_steps": 500, | |
| "global_step": 1286, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 0.78, | |
| "grad_norm": 7.225562572479248, | |
| "learning_rate": 4.740798341109383e-05, | |
| "loss": 1.2727, | |
| "step": 500 | |
| }, | |
| { | |
| "epoch": 1.0, | |
| "eval_accuracy": 0.7327652982184353, | |
| "eval_f1_macro": 0.4979866910649434, | |
| "eval_f1_micro": 0.7327652982184353, | |
| "eval_f1_weighted": 0.7112918103899314, | |
| "eval_loss": 0.9499744176864624, | |
| "eval_macro_fpr": 0.026781755579062278, | |
| "eval_macro_sensitivity": 0.5114002497331291, | |
| "eval_macro_specificity": 0.9794963249743184, | |
| "eval_precision": 0.7197802809496298, | |
| "eval_precision_macro": 0.5174669881422742, | |
| "eval_recall": 0.7327652982184353, | |
| "eval_recall_macro": 0.5114002497331291, | |
| "eval_runtime": 30.8761, | |
| "eval_samples_per_second": 41.812, | |
| "eval_steps_per_second": 5.247, | |
| "eval_weighted_fpr": 0.02538818161748473, | |
| "eval_weighted_sensitivity": 0.7327652982184353, | |
| "eval_weighted_specificity": 0.9596795763963419, | |
| "step": 643 | |
| }, | |
| { | |
| "epoch": 1.56, | |
| "grad_norm": 36.07024383544922, | |
| "learning_rate": 4.481596682218767e-05, | |
| "loss": 0.7445, | |
| "step": 1000 | |
| }, | |
| { | |
| "epoch": 2.0, | |
| "eval_accuracy": 0.7544539116963594, | |
| "eval_f1_macro": 0.660482894909786, | |
| "eval_f1_micro": 0.7544539116963594, | |
| "eval_f1_weighted": 0.7570154170830723, | |
| "eval_loss": 0.8124240040779114, | |
| "eval_macro_fpr": 0.022813641549666076, | |
| "eval_macro_sensitivity": 0.6711837410973555, | |
| "eval_macro_specificity": 0.9817878301326088, | |
| "eval_precision": 0.7742972626232849, | |
| "eval_precision_macro": 0.6650265760635443, | |
| "eval_recall": 0.7544539116963594, | |
| "eval_recall_macro": 0.6711837410973555, | |
| "eval_runtime": 52.9896, | |
| "eval_samples_per_second": 24.363, | |
| "eval_steps_per_second": 3.057, | |
| "eval_weighted_fpr": 0.02271912850283093, | |
| "eval_weighted_sensitivity": 0.7544539116963594, | |
| "eval_weighted_specificity": 0.9723635402927732, | |
| "step": 1286 | |
| } | |
| ], | |
| "logging_steps": 500, | |
| "max_steps": 9645, | |
| "num_input_tokens_seen": 0, | |
| "num_train_epochs": 15, | |
| "save_steps": 500, | |
| "total_flos": 2704044788895744.0, | |
| "train_batch_size": 8, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |