Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use AndrewDOrlov/bert-eval-256 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AndrewDOrlov/bert-eval-256 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AndrewDOrlov/bert-eval-256")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("AndrewDOrlov/bert-eval-256") model = AutoModelForSequenceClassification.from_pretrained("AndrewDOrlov/bert-eval-256", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "rubert_cased_L-12_H-768_A-12_pt", | |
| "architectures": [ | |
| "BertForSequenceClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "classifier_dropout": null, | |
| "directionality": "bidi", | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "id2label": { | |
| "0": "\u0437\u0430\u043a\u043e\u043d\u044b_\u0438_\u043d\u0430\u0440\u0443\u0448\u0435\u043d\u0438\u044f", | |
| "1": "\u0440\u0430\u0437\u0440\u0430\u0431\u043e\u0442\u043a\u0430", | |
| "2": "\u0438\u0441\u0441\u043b\u0435\u0434\u043e\u0432\u0430\u043d\u0438\u044f", | |
| "3": "\u0432\u0440\u0435\u0434\u043e\u043d\u043e\u0441\u044b", | |
| "4": "\u0443\u044f\u0437\u0432\u0438\u043c\u043e\u0441\u0442\u0438", | |
| "5": "\u0443\u0442\u0435\u0447\u043a\u0438", | |
| "6": "\u043c\u043e\u0448\u0435\u043d\u043d\u0438\u0447\u0435\u0441\u0442\u0432\u0430", | |
| "7": "\u0433\u043e\u0441\u0440\u0435\u0433\u0443\u043b\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0435", | |
| "8": "\u043f\u0440\u043e\u0433\u0440\u0430\u043c\u043c\u043d\u043e\u0435_\u043e\u0431\u0435\u0441\u043f\u0435\u0447\u0435\u043d\u0438\u0435", | |
| "9": "\u0444\u0438\u043d\u0430\u043d\u0441\u044b_\u0438_\u0431\u0438\u0437\u043d\u0435\u0441", | |
| "10": "\u0441\u043e\u0431\u044b\u0442\u0438\u044f_\u0438_\u043c\u0435\u0440\u043e\u043f\u0440\u0438\u044f\u0442\u0438\u044f", | |
| "11": "\u043a\u0438\u0431\u0435\u0440\u0431\u0435\u0437\u043e\u043f\u0430\u0441\u043d\u043e\u0441\u0442\u044c" | |
| }, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "label2id": { | |
| "\u0432\u0440\u0435\u0434\u043e\u043d\u043e\u0441\u044b": 3, | |
| "\u0433\u043e\u0441\u0440\u0435\u0433\u0443\u043b\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0435": 7, | |
| "\u0437\u0430\u043a\u043e\u043d\u044b_\u0438_\u043d\u0430\u0440\u0443\u0448\u0435\u043d\u0438\u044f": 0, | |
| "\u0438\u0441\u0441\u043b\u0435\u0434\u043e\u0432\u0430\u043d\u0438\u044f": 2, | |
| "\u043a\u0438\u0431\u0435\u0440\u0431\u0435\u0437\u043e\u043f\u0430\u0441\u043d\u043e\u0441\u0442\u044c": 11, | |
| "\u043c\u043e\u0448\u0435\u043d\u043d\u0438\u0447\u0435\u0441\u0442\u0432\u0430": 6, | |
| "\u043f\u0440\u043e\u0433\u0440\u0430\u043c\u043c\u043d\u043e\u0435_\u043e\u0431\u0435\u0441\u043f\u0435\u0447\u0435\u043d\u0438\u0435": 8, | |
| "\u0440\u0430\u0437\u0440\u0430\u0431\u043e\u0442\u043a\u0430": 1, | |
| "\u0441\u043e\u0431\u044b\u0442\u0438\u044f_\u0438_\u043c\u0435\u0440\u043e\u043f\u0440\u0438\u044f\u0442\u0438\u044f": 10, | |
| "\u0443\u0442\u0435\u0447\u043a\u0438": 5, | |
| "\u0443\u044f\u0437\u0432\u0438\u043c\u043e\u0441\u0442\u0438": 4, | |
| "\u0444\u0438\u043d\u0430\u043d\u0441\u044b_\u0438_\u0431\u0438\u0437\u043d\u0435\u0441": 9 | |
| }, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 512, | |
| "model_type": "bert", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "pad_token_id": 0, | |
| "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": "multi_label_classification", | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.30.2", | |
| "type_vocab_size": 2, | |
| "use_cache": true, | |
| "vocab_size": 119547 | |
| } | |