Token Classification
Transformers
Safetensors
Romanian
xlm-roberta
relation-extraction
romanian
span-detection
ner
Instructions to use DS4AI-UPB/span-detector-ro with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DS4AI-UPB/span-detector-ro with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="DS4AI-UPB/span-detector-ro")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("DS4AI-UPB/span-detector-ro") model = AutoModelForTokenClassification.from_pretrained("DS4AI-UPB/span-detector-ro", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 954 Bytes
6afc71a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 | {
"add_cross_attention": false,
"architectures": [
"XLMRobertaForTokenClassification"
],
"attention_probs_dropout_prob": 0.1,
"bos_token_id": 0,
"classifier_dropout": null,
"dtype": "float32",
"eos_token_id": 2,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 1024,
"id2label": {
"0": "O",
"1": "B-E1",
"2": "I-E1",
"3": "B-E2",
"4": "I-E2"
},
"initializer_range": 0.02,
"intermediate_size": 4096,
"is_decoder": false,
"label2id": {
"B-E1": 1,
"B-E2": 3,
"I-E1": 2,
"I-E2": 4,
"O": 0
},
"layer_norm_eps": 1e-05,
"max_position_embeddings": 514,
"model_type": "xlm-roberta",
"num_attention_heads": 16,
"num_hidden_layers": 24,
"output_past": true,
"pad_token_id": 1,
"position_embedding_type": "absolute",
"tie_word_embeddings": true,
"transformers_version": "5.5.0",
"type_vocab_size": 1,
"use_cache": false,
"vocab_size": 250002
}
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