Text Classification
Transformers
TensorBoard
Safetensors
deberta-v2
Trained with AutoTrain
text-embeddings-inference
Instructions to use idobn/twitter-mbti-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use idobn/twitter-mbti-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="idobn/twitter-mbti-v2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("idobn/twitter-mbti-v2") model = AutoModelForSequenceClassification.from_pretrained("idobn/twitter-mbti-v2") - Notebooks
- Google Colab
- Kaggle
| { | |
| "data_path": "twitter-mbti-v2/autotrain-data", | |
| "model": "microsoft/deberta-v3-large", | |
| "lr": 1e-05, | |
| "epochs": 10, | |
| "max_seq_length": 512, | |
| "batch_size": 4, | |
| "warmup_ratio": 0.1, | |
| "gradient_accumulation": 8, | |
| "optimizer": "adamw_torch", | |
| "scheduler": "linear", | |
| "weight_decay": 0.01, | |
| "max_grad_norm": 1.0, | |
| "seed": 42, | |
| "train_split": "train", | |
| "valid_split": "validation", | |
| "text_column": "autotrain_text", | |
| "target_column": "autotrain_label", | |
| "logging_steps": 10, | |
| "project_name": "twitter-mbti-v2", | |
| "auto_find_batch_size": false, | |
| "mixed_precision": "fp16", | |
| "save_total_limit": 1, | |
| "push_to_hub": true, | |
| "eval_strategy": "epoch", | |
| "username": "idobn", | |
| "log": "tensorboard", | |
| "early_stopping_patience": 5, | |
| "early_stopping_threshold": 0.01 | |
| } |