Text Classification
Transformers
PyTorch
Safetensors
English
bert
Trained with AutoTrain
text-embeddings-inference
Instructions to use badalsahani/pdf-classification-multi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use badalsahani/pdf-classification-multi with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="badalsahani/pdf-classification-multi")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("badalsahani/pdf-classification-multi") model = AutoModelForSequenceClassification.from_pretrained("badalsahani/pdf-classification-multi") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "AutoTrain", | |
| "_num_labels": 12, | |
| "architectures": [ | |
| "BertForSequenceClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "classifier_dropout": null, | |
| "directionality": "bidi", | |
| "gradient_checkpointing": false, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 1024, | |
| "id2label": { | |
| "0": "accountancy", | |
| "1": "business-studies", | |
| "2": "computer science", | |
| "3": "economics", | |
| "4": "english literature", | |
| "5": "geography", | |
| "6": "history", | |
| "7": "mathematics", | |
| "8": "political science", | |
| "9": "psychology", | |
| "10": "science", | |
| "11": "sociology" | |
| }, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 4096, | |
| "label2id": { | |
| "accountancy": 0, | |
| "business-studies": 1, | |
| "computer science": 2, | |
| "economics": 3, | |
| "english literature": 4, | |
| "geography": 5, | |
| "history": 6, | |
| "mathematics": 7, | |
| "political science": 8, | |
| "psychology": 9, | |
| "science": 10, | |
| "sociology": 11 | |
| }, | |
| "layer_norm_eps": 1e-12, | |
| "max_length": 512, | |
| "max_position_embeddings": 512, | |
| "model_type": "bert", | |
| "num_attention_heads": 16, | |
| "num_hidden_layers": 24, | |
| "pad_token_id": 0, | |
| "padding": "max_length", | |
| "pooler_fc_size": 768, | |
| "pooler_num_attention_heads": 12, | |
| "pooler_num_fc_layers": 3, | |
| "pooler_size_per_head": 128, | |
| "pooler_type": "first_token_transform", | |
| "position_embedding_type": "absolute", | |
| "problem_type": "single_label_classification", | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.25.1", | |
| "type_vocab_size": 2, | |
| "use_cache": true, | |
| "vocab_size": 28996 | |
| } | |