Text Classification
Transformers
Safetensors
Chinese
bert
agent
nlp
chinese
sentiment-analysis
emotion
regression
vad
valence-arousal-dominance
macbert
text-embeddings-inference
Instructions to use Pectics/vad-macbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Pectics/vad-macbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Pectics/vad-macbert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Pectics/vad-macbert") model = AutoModelForSequenceClassification.from_pretrained("Pectics/vad-macbert") - Notebooks
- Google Colab
- Kaggle
| { | |
| "batch_size": 32, | |
| "data_path": "train/en-zh_cn_vad_mix.csv", | |
| "dtype": "fp16", | |
| "encoding": "utf-8", | |
| "epochs": 4, | |
| "errors": "ignore", | |
| "eval_batch_size": 0, | |
| "eval_batches": 200, | |
| "eval_every": 100, | |
| "eval_ratio": 0.01, | |
| "grad_accum_steps": 4, | |
| "huber_delta": 1.0, | |
| "learning_rate": 1e-05, | |
| "log_every": 1, | |
| "loss": "huber", | |
| "max_length": 512, | |
| "max_rows": null, | |
| "max_steps": 5000, | |
| "min_chars": 2, | |
| "model_name": "hfl/chinese-macbert-base", | |
| "num_labels": 3, | |
| "num_rows": 400000, | |
| "output_dir": "train/vad-macbert-mix", | |
| "resume_from": "train/vad-macbert-long/best", | |
| "save_every": 100, | |
| "seed": 42, | |
| "shuffle_buffer": 4096, | |
| "warmup_ratio": 0.1, | |
| "warmup_steps": 0, | |
| "weight_decay": 0.01 | |
| } |