Instructions to use hiroki-rad/bert-base-classification-ft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hiroki-rad/bert-base-classification-ft with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hiroki-rad/bert-base-classification-ft")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hiroki-rad/bert-base-classification-ft") model = AutoModelForSequenceClassification.from_pretrained("hiroki-rad/bert-base-classification-ft", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from hiroki-rad/bert-base-classification-ft: direct link, hf CLI and curl.
- Browser
- Download file 1.25 kB
-
https://huggingface.co/hiroki-rad/bert-base-classification-ft/resolve/main/config.json
- Command line
-
hf download hf://hiroki-rad/bert-base-classification-ft/config.json
-
curl -L -o config.json https://huggingface.co/hiroki-rad/bert-base-classification-ft/resolve/main/config.json
1.25 kB
| { | |
| "_name_or_path": "cl-tohoku/bert-base-japanese-v3", | |
| "architectures": [ | |
| "BertForSequenceClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "classifier_dropout": null, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "id2label": { | |
| "0": "Task_Solution", | |
| "1": "Creative_Generation", | |
| "2": "Knowledge_Explanation", | |
| "3": "Analytical_Reasoning", | |
| "4": "Information_Extraction", | |
| "5": "Step_by_Step_Calculation", | |
| "6": "Role_Play_Response", | |
| "7": "Opinion_Perspective" | |
| }, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "label2id": { | |
| "Analytical_Reasoning": 3, | |
| "Creative_Generation": 1, | |
| "Information_Extraction": 4, | |
| "Knowledge_Explanation": 2, | |
| "Opinion_Perspective": 7, | |
| "Role_Play_Response": 6, | |
| "Step_by_Step_Calculation": 5, | |
| "Task_Solution": 0 | |
| }, | |
| "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": "4.46.3", | |
| "type_vocab_size": 2, | |
| "use_cache": true, | |
| "vocab_size": 32768 | |
| } | |