Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use Silicon23/BERTForDetectingDepression-Twitter2015 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Silicon23/BERTForDetectingDepression-Twitter2015 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Silicon23/BERTForDetectingDepression-Twitter2015")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Silicon23/BERTForDetectingDepression-Twitter2015") model = AutoModelForSequenceClassification.from_pretrained("Silicon23/BERTForDetectingDepression-Twitter2015") - Notebooks
- Google Colab
- Kaggle
| { | |
| "best_metric": 0.7826887661141805, | |
| "best_model_checkpoint": "BERTForDetectingDepression-Twitter2015/run-0/checkpoint-184", | |
| "epoch": 1.0, | |
| "eval_steps": 500, | |
| "global_step": 184, | |
| "is_hyper_param_search": true, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 1.0, | |
| "eval_accuracy": 0.7826887661141805, | |
| "eval_loss": 0.45181456208229065, | |
| "eval_runtime": 1.6866, | |
| "eval_samples_per_second": 965.826, | |
| "eval_steps_per_second": 60.475, | |
| "step": 184 | |
| } | |
| ], | |
| "logging_steps": 500, | |
| "max_steps": 736, | |
| "num_input_tokens_seen": 0, | |
| "num_train_epochs": 4, | |
| "save_steps": 500, | |
| "stateful_callbacks": { | |
| "TrainerControl": { | |
| "args": { | |
| "should_epoch_stop": false, | |
| "should_evaluate": false, | |
| "should_log": false, | |
| "should_save": true, | |
| "should_training_stop": false | |
| }, | |
| "attributes": {} | |
| } | |
| }, | |
| "total_flos": 0, | |
| "train_batch_size": 8, | |
| "trial_name": null, | |
| "trial_params": { | |
| "learning_rate": 1.0817235137868972e-05, | |
| "num_train_epochs": 4, | |
| "per_device_train_batch_size": 8, | |
| "seed": 5 | |
| } | |
| } | |