Text Classification
Transformers
Safetensors
English
bert
customer-feedback
aspect-based-sentiment-analysis
text-embeddings-inference
Instructions to use jiangzy1881/aspect-detection-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jiangzy1881/aspect-detection-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jiangzy1881/aspect-detection-model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jiangzy1881/aspect-detection-model") model = AutoModelForSequenceClassification.from_pretrained("jiangzy1881/aspect-detection-model") - Notebooks
- Google Colab
- Kaggle
| { | |
| "add_cross_attention": false, | |
| "architectures": [ | |
| "BertForSequenceClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "bos_token_id": null, | |
| "classifier_dropout": null, | |
| "dtype": "float32", | |
| "eos_token_id": null, | |
| "gradient_checkpointing": false, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "id2label": { | |
| "0": "food", | |
| "1": "menu", | |
| "2": "service", | |
| "3": "staff", | |
| "4": "price", | |
| "5": "place", | |
| "6": "ambience", | |
| "7": "waiting", | |
| "8": "miscellaneous" | |
| }, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "is_decoder": false, | |
| "label2id": { | |
| "ambience": 6, | |
| "food": 0, | |
| "menu": 1, | |
| "miscellaneous": 8, | |
| "place": 5, | |
| "price": 4, | |
| "service": 2, | |
| "staff": 3, | |
| "waiting": 7 | |
| }, | |
| "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": "multi_label_classification", | |
| "tie_word_embeddings": true, | |
| "transformers_version": "5.0.0", | |
| "type_vocab_size": 2, | |
| "use_cache": false, | |
| "vocab_size": 30522 | |
| } | |