Instructions to use ekolasky/CustomLEDForResultsIdOnSeperatedSet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ekolasky/CustomLEDForResultsIdOnSeperatedSet with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ekolasky/CustomLEDForResultsIdOnSeperatedSet", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("ekolasky/CustomLEDForResultsIdOnSeperatedSet", trust_remote_code=True) model = AutoModel.from_pretrained("ekolasky/CustomLEDForResultsIdOnSeperatedSet", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
Update handler.py
Browse files- handler.py +2 -2
handler.py
CHANGED
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@@ -52,8 +52,8 @@ class EndpointHandler():
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result_array = []
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if (indices_array):
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for result_indices in indices_array:
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text = self.tokenizer.decode(input_ids[result_indices[0]:result_indices[-1]])
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indices = [offset_mapping[result_indices[0]][0], offset_mapping[result_indices[-1]][1]]
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if text != "" and not text.isspace():
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while True:
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if text[0] == " ":
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result_array = []
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if (indices_array):
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for result_indices in indices_array:
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text = self.tokenizer.decode(input_ids[result_indices[0]:result_indices[-1]+1])
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indices = [offset_mapping[result_indices[0]][0], offset_mapping[result_indices[-1]+1][1]]
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if text != "" and not text.isspace():
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while True:
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if text[0] == " ":
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