Text Classification
Transformers
Safetensors
English
distilbert
distilbert-base-uncased
text-embeddings-inference
Instructions to use disham993/electrical-classification-distilbert-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use disham993/electrical-classification-distilbert-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="disham993/electrical-classification-distilbert-base")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("disham993/electrical-classification-distilbert-base") model = AutoModelForSequenceClassification.from_pretrained("disham993/electrical-classification-distilbert-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| {"_name_or_path": "distilbert/distilbert-base-uncased", "activation": "gelu", "architectures": ["DistilBertForSequenceClassification"], "attention_dropout": 0.1, "dim": 768, "dropout": 0.1, "hidden_dim": 3072, "id2label": {"0": "negative", "1": "positive", "2": "mixed", "3": "neutral"}, "initializer_range": 0.02, "label2id": {"negative": "0", "positive": "1", "mixed": "2", "neutral": "3"}, "max_position_embeddings": 512, "model_type": "distilbert", "n_heads": 12, "n_layers": 6, "pad_token_id": 0, "problem_type": "single_label_classification", "qa_dropout": 0.1, "seq_classif_dropout": 0.2, "sinusoidal_pos_embds": false, "tie_weights_": true, "torch_dtype": "float32", "transformers_version": "4.48.0.dev0", "vocab_size": 30522} |