Instructions to use dl-ru/rubert-tiny2-srl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dl-ru/rubert-tiny2-srl with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="dl-ru/rubert-tiny2-srl")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("dl-ru/rubert-tiny2-srl") model = AutoModelForTokenClassification.from_pretrained("dl-ru/rubert-tiny2-srl") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "cointegrated/rubert-tiny2", | |
| "architectures": [ | |
| "BertForTokenClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "classifier_dropout": null, | |
| "emb_size": 312, | |
| "gradient_checkpointing": false, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 312, | |
| "id2label": { | |
| "0": "O", | |
| "1": "B-Predicate", | |
| "2": "B-Object", | |
| "3": "B-Experiencer", | |
| "4": "B-Cause", | |
| "5": "B-Deliberative", | |
| "6": "B-Causator", | |
| "7": "B-ContrSubject", | |
| "8": "B-Benefactive", | |
| "9": "B-Addressee" | |
| }, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 600, | |
| "label2id": { | |
| "B-Addressee": 9, | |
| "B-Benefactive": 8, | |
| "B-Causator": 6, | |
| "B-Cause": 4, | |
| "B-ContrSubject": 7, | |
| "B-Deliberative": 5, | |
| "B-Experiencer": 3, | |
| "B-Object": 2, | |
| "B-Predicate": 1, | |
| "O": 0 | |
| }, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 2048, | |
| "model_type": "bert", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 3, | |
| "pad_token_id": 0, | |
| "position_embedding_type": "absolute", | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.28.1", | |
| "type_vocab_size": 2, | |
| "use_cache": true, | |
| "vocab_size": 83828 | |
| } | |