Instructions to use Aleksandar/electra-srb-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Aleksandar/electra-srb-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Aleksandar/electra-srb-ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Aleksandar/electra-srb-ner") model = AutoModelForTokenClassification.from_pretrained("Aleksandar/electra-srb-ner") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "Aleksandar/electra-srb-oscar", | |
| "architectures": [ | |
| "ElectraForTokenClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "embedding_size": 768, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "id2label": { | |
| "0": "O", | |
| "1": "B-per", | |
| "2": "I-per", | |
| "3": "B-org", | |
| "4": "I-org", | |
| "5": "B-loc", | |
| "6": "I-loc" | |
| }, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "label2id": { | |
| "LABEL_0": 0, | |
| "LABEL_1": 1, | |
| "LABEL_2": 2, | |
| "LABEL_3": 3, | |
| "LABEL_4": 4, | |
| "LABEL_5": 5, | |
| "LABEL_6": 6 | |
| }, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 512, | |
| "model_type": "electra", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "pad_token_id": 0, | |
| "position_embedding_type": "absolute", | |
| "summary_activation": "gelu", | |
| "summary_last_dropout": 0.1, | |
| "summary_type": "first", | |
| "summary_use_proj": true, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.9.2", | |
| "type_vocab_size": 2, | |
| "vocab_size": 30522 | |
| } | |