Text Classification
Transformers
TensorBoard
Safetensors
bert
Trained with AutoTrain
text-embeddings-inference
Instructions to use berwart/Emotion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use berwart/Emotion with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="berwart/Emotion")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("berwart/Emotion") model = AutoModelForSequenceClassification.from_pretrained("berwart/Emotion") - Notebooks
- Google Colab
- Kaggle
| { | |
| "data_path": "dair-ai/emotion", | |
| "model": "google-bert/bert-base-uncased", | |
| "lr": 5e-05, | |
| "epochs": 3, | |
| "max_seq_length": 128, | |
| "batch_size": 8, | |
| "warmup_ratio": 0.1, | |
| "gradient_accumulation": 1, | |
| "optimizer": "sgd", | |
| "scheduler": "constant", | |
| "weight_decay": 0.0, | |
| "max_grad_norm": 1.0, | |
| "seed": 42, | |
| "train_split": "train", | |
| "valid_split": null, | |
| "text_column": "text", | |
| "target_column": "label", | |
| "logging_steps": 1, | |
| "project_name": "autotrain-y6xip-x1g7s", | |
| "auto_find_batch_size": true, | |
| "mixed_precision": "fp16", | |
| "save_total_limit": 1, | |
| "push_to_hub": true, | |
| "eval_strategy": "epoch", | |
| "username": "mxersion", | |
| "log": "tensorboard", | |
| "early_stopping_patience": 5, | |
| "early_stopping_threshold": 0.01 | |
| } |