Instructions to use RecordedFuture/Swedish-NER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RecordedFuture/Swedish-NER with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="RecordedFuture/Swedish-NER")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("RecordedFuture/Swedish-NER") model = AutoModelForTokenClassification.from_pretrained("RecordedFuture/Swedish-NER") - Notebooks
- Google Colab
- Kaggle
Commit ·
436f9d5
1
Parent(s): 6ed3d78
added num labels to config
Browse files- config.json +1 -0
config.json
CHANGED
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@@ -9,6 +9,7 @@
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 768,
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"id2label": {
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"0": "B-LOC",
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"1": "B-NAT",
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 768,
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"_num_labels": 15,
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"id2label": {
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"0": "B-LOC",
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"1": "B-NAT",
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