Instructions to use rajendrabaskota/bert-base-dataverse-2023 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rajendrabaskota/bert-base-dataverse-2023 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rajendrabaskota/bert-base-dataverse-2023")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rajendrabaskota/bert-base-dataverse-2023") model = AutoModelForSequenceClassification.from_pretrained("rajendrabaskota/bert-base-dataverse-2023") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "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": "cs", | |
| "1": "math", | |
| "2": "physics", | |
| "3": "eess", | |
| "4": "cond-mat", | |
| "5": "q-bio", | |
| "6": "stat", | |
| "7": "hep-ph", | |
| "8": "math-ph", | |
| "9": "hep-th", | |
| "10": "astro-ph", | |
| "11": "gr-qc", | |
| "12": "nlin", | |
| "13": "quant-ph", | |
| "14": "nucl-th", | |
| "15": "q-fin", | |
| "16": "nucl-ex", | |
| "17": "hep-ex", | |
| "18": "econ", | |
| "19": "hep-lat" | |
| }, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "label2id": { | |
| "astro-ph": 10, | |
| "cond-mat": 4, | |
| "cs": 0, | |
| "econ": 18, | |
| "eess": 3, | |
| "gr-qc": 11, | |
| "hep-ex": 17, | |
| "hep-lat": 19, | |
| "hep-ph": 7, | |
| "hep-th": 9, | |
| "math": 1, | |
| "math-ph": 8, | |
| "nlin": 12, | |
| "nucl-ex": 16, | |
| "nucl-th": 14, | |
| "physics": 2, | |
| "q-bio": 5, | |
| "q-fin": 15, | |
| "quant-ph": 13, | |
| "stat": 6 | |
| }, | |
| "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": "4.20.1", | |
| "type_vocab_size": 2, | |
| "use_cache": true, | |
| "vocab_size": 30522 | |
| } | |