Instructions to use WilliamWen/ni_final with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use WilliamWen/ni_final with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="WilliamWen/ni_final")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("WilliamWen/ni_final") model = AutoModelForTokenClassification.from_pretrained("WilliamWen/ni_final", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit ·
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Parent(s): e4deb57
Delete config.json
Browse files- config.json +0 -43
config.json
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{
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"_name_or_path": "AutoTrain",
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"_num_labels": 2,
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"architectures": [
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"BertForTokenClassification"
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],
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"attention_probs_dropout_prob": 0.1,
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"classifier_dropout": null,
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"directionality": "bidi",
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"gradient_checkpointing": false,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 1024,
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"id2label": {
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"0": "IN-Ni",
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"1": "O"
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},
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"initializer_range": 0.02,
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"intermediate_size": 4096,
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"label2id": {
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"IN-Ni": 0,
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"O": 1
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},
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"layer_norm_eps": 1e-12,
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"max_length": 384,
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"max_position_embeddings": 512,
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"model_type": "bert",
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"num_attention_heads": 16,
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"num_hidden_layers": 24,
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"pad_token_id": 0,
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"padding": "max_length",
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"pooler_fc_size": 768,
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"pooler_num_attention_heads": 12,
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"pooler_num_fc_layers": 3,
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"pooler_size_per_head": 128,
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"pooler_type": "first_token_transform",
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"position_embedding_type": "absolute",
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"torch_dtype": "float32",
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"transformers_version": "4.25.1",
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"type_vocab_size": 2,
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"use_cache": true,
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"vocab_size": 28996
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}
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