Text Classification
Transformers
Safetensors
English
bert
bert-base-uncased
text-embeddings-inference
Instructions to use disham993/electrical-classification-bert-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use disham993/electrical-classification-bert-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="disham993/electrical-classification-bert-base")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("disham993/electrical-classification-bert-base") model = AutoModelForSequenceClassification.from_pretrained("disham993/electrical-classification-bert-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| {"_name_or_path": "google-bert/bert-base-uncased", "architectures": ["BertForSequenceClassification"], "attention_probs_dropout_prob": 0.1, "classifier_dropout": null, "gradient_checkpointing": false, "hidden_act": "gelu", "hidden_dropout_prob": 0.1, "hidden_size": 768, "id2label": {"0": "negative", "1": "positive", "2": "mixed", "3": "neutral"}, "initializer_range": 0.02, "intermediate_size": 3072, "label2id": {"negative": "0", "positive": "1", "mixed": "2", "neutral": "3"}, "layer_norm_eps": 1e-12, "max_position_embeddings": 512, "model_type": "bert", "num_attention_heads": 12, "num_hidden_layers": 12, "pad_token_id": 0, "position_embedding_type": "absolute", "problem_type": "single_label_classification", "torch_dtype": "float32", "transformers_version": "4.48.0.dev0", "type_vocab_size": 2, "use_cache": true, "vocab_size": 30522} |