Instructions to use mrmorenom/BERT-3Class-SC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mrmorenom/BERT-3Class-SC with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mrmorenom/BERT-3Class-SC")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mrmorenom/BERT-3Class-SC") model = AutoModelForSequenceClassification.from_pretrained("mrmorenom/BERT-3Class-SC", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "./bert_label_model_3class", | |
| "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": "\u0628\u06cc\u200c\u0631\u0628\u0637 (\u062e\u0646\u062b\u06cc)", | |
| "1": "\u0622\u0631\u0627\u0645\u0634 \u0648 \u0628\u0627\u0632\u06af\u0634\u062a \u0628\u0647 \u0634\u0647\u0631", | |
| "2": "\u0628\u062d\u0631\u0627\u0646 \u0648 \u062a\u062e\u0644\u06cc\u0647 \u0634\u0647\u0631" | |
| }, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "label2id": { | |
| "\u0622\u0631\u0627\u0645\u0634 \u0648 \u0628\u0627\u0632\u06af\u0634\u062a \u0628\u0647 \u0634\u0647\u0631": 1, | |
| "\u0628\u062d\u0631\u0627\u0646 \u0648 \u062a\u062e\u0644\u06cc\u0647 \u0634\u0647\u0631": 2, | |
| "\u0628\u06cc\u200c\u0631\u0628\u0637 (\u062e\u0646\u062b\u06cc)": 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": 42000 | |
| } | |