Instructions to use moosemandada/tiny-ner-probe with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use moosemandada/tiny-ner-probe with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="moosemandada/tiny-ner-probe")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("moosemandada/tiny-ner-probe") model = AutoModelForTokenClassification.from_pretrained("moosemandada/tiny-ner-probe", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,672 Bytes
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"architectures": [
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],
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"directionality": "bidi",
"gradient_checkpointing": false,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 2,
"id2label": {
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"label2id": {
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"B-MISC": 1,
"B-ORG": 5,
"B-PER": 3,
"I-LOC": 8,
"I-MISC": 2,
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"O": 0
},
"layer_norm_eps": 1e-12,
"max_position_embeddings": 512,
"model_type": "bert",
"num_attention_heads": 2,
"num_hidden_layers": 2,
"pad_token_id": 0,
"pooler_fc_size": 768,
"pooler_num_attention_heads": 12,
"pooler_num_fc_layers": 3,
"pooler_size_per_head": 128,
"pooler_type": "first_token_transform",
"position_embedding_type": "absolute",
"transformers_version": "4.27.0.dev0",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 28996
}
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