Token Classification
SpanMarker
TensorBoard
Safetensors
English
ner
named-entity-recognition
generated_from_span_marker_trainer
Eval Results (legacy)
Instructions to use LegionIntel/ner-document-context with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- SpanMarker
How to use LegionIntel/ner-document-context with SpanMarker:
from span_marker import SpanMarkerModel model = SpanMarkerModel.from_pretrained("LegionIntel/ner-document-context") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "RobertaForMaskedLM" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "bos_token_id": 0, | |
| "eos_token_id": 2, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 1024, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 4096, | |
| "layer_norm_eps": 1e-05, | |
| "max_position_embeddings": 514, | |
| "model_type": "roberta", | |
| "num_attention_heads": 16, | |
| "num_hidden_layers": 24, | |
| "pad_token_id": 1, | |
| "type_vocab_size": 1, | |
| "vocab_size": 50265 | |
| } | |