Instructions to use syssec-utd/py313-pylingual-v1-segmenter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use syssec-utd/py313-pylingual-v1-segmenter with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="syssec-utd/py313-pylingual-v1-segmenter")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("syssec-utd/py313-pylingual-v1-segmenter") model = AutoModelForTokenClassification.from_pretrained("syssec-utd/py313-pylingual-v1-segmenter") - Notebooks
- Google Colab
- Kaggle
File size: 828 Bytes
0d5baff | 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 | {
"_name_or_path": "syssec-utd/py313-pylingual-v1-mlm",
"architectures": [
"RobertaForTokenClassification"
],
"attention_probs_dropout_prob": 0.1,
"bos_token_id": 0,
"classifier_dropout": null,
"eos_token_id": 2,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"id2label": {
"0": "B",
"1": "I",
"2": "E"
},
"initializer_range": 0.02,
"intermediate_size": 3072,
"label2id": {
"B": "0",
"E": "2",
"I": "1"
},
"layer_norm_eps": 1e-05,
"max_position_embeddings": 514,
"model_type": "roberta",
"num_attention_heads": 12,
"num_hidden_layers": 12,
"pad_token_id": 1,
"position_embedding_type": "absolute",
"torch_dtype": "float32",
"transformers_version": "4.48.2",
"type_vocab_size": 1,
"use_cache": true,
"vocab_size": 30000
}
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