Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use xshubhamx/InLegalBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xshubhamx/InLegalBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="xshubhamx/InLegalBERT")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("xshubhamx/InLegalBERT") model = AutoModelForSequenceClassification.from_pretrained("xshubhamx/InLegalBERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "best_metric": 0.6028764374506596, | |
| "best_model_checkpoint": "InLegalBERT/checkpoint-643", | |
| "epoch": 1.0, | |
| "eval_steps": 500, | |
| "global_step": 643, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 0.78, | |
| "grad_norm": 6.225058078765869, | |
| "learning_rate": 4.742871954380509e-05, | |
| "loss": 1.065, | |
| "step": 500 | |
| }, | |
| { | |
| "epoch": 1.0, | |
| "eval_accuracy": 0.7993803253292022, | |
| "eval_f1_macro": 0.6028764374506596, | |
| "eval_f1_micro": 0.7993803253292022, | |
| "eval_f1_weighted": 0.780434475079005, | |
| "eval_loss": 0.6395189166069031, | |
| "eval_macro_fpr": 0.01853296480715729, | |
| "eval_macro_sensitivity": 0.6307714529979154, | |
| "eval_macro_specificity": 0.9847175193097543, | |
| "eval_precision": 0.7817573111259606, | |
| "eval_precision_macro": 0.6194286814679265, | |
| "eval_recall": 0.7993803253292022, | |
| "eval_recall_macro": 0.6307714529979154, | |
| "eval_runtime": 29.4696, | |
| "eval_samples_per_second": 43.808, | |
| "eval_steps_per_second": 5.497, | |
| "eval_weighted_fpr": 0.017610661589719183, | |
| "eval_weighted_sensitivity": 0.7993803253292022, | |
| "eval_weighted_specificity": 0.9713824643171124, | |
| "step": 643 | |
| } | |
| ], | |
| "logging_steps": 500, | |
| "max_steps": 9645, | |
| "num_input_tokens_seen": 0, | |
| "num_train_epochs": 15, | |
| "save_steps": 500, | |
| "total_flos": 1352022394447872.0, | |
| "train_batch_size": 8, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |