Instructions to use preneond/newlinefix-encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use preneond/newlinefix-encoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="preneond/newlinefix-encoder")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("preneond/newlinefix-encoder") model = AutoModelForTokenClassification.from_pretrained("preneond/newlinefix-encoder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 862 Bytes
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"add_cross_attention": false,
"architectures": [
"RobertaForTokenClassification"
],
"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": 768,
"id2label": {
"0": "JOIN",
"1": "SPACE",
"2": "NEWLINE",
"3": "PARA"
},
"initializer_range": 0.02,
"intermediate_size": 3072,
"is_decoder": false,
"label2id": {
"JOIN": 0,
"NEWLINE": 2,
"PARA": 3,
"SPACE": 1
},
"layer_norm_eps": 1e-05,
"max_position_embeddings": 514,
"model_type": "roberta",
"num_attention_heads": 12,
"num_hidden_layers": 6,
"pad_token_id": 1,
"tie_word_embeddings": true,
"transformers_version": "5.15.0",
"type_vocab_size": 1,
"use_cache": true,
"vocab_size": 50265
}
|