Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use HCKLab/BiBert-MultiTask-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HCKLab/BiBert-MultiTask-1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="HCKLab/BiBert-MultiTask-1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("HCKLab/BiBert-MultiTask-1") model = AutoModelForSequenceClassification.from_pretrained("HCKLab/BiBert-MultiTask-1") - Notebooks
- Google Colab
- Kaggle
Update handler.py
Browse files- handler.py +1 -1
handler.py
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@@ -217,7 +217,7 @@ class EndpointHandler():
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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tokenizer = AutoTokenizer.from_pretrained(
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tasks = [
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Task(id=0, name='label_classification', type='seq_classification', num_labels=5),
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)
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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tokenizer = AutoTokenizer.from_pretrained(path)
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tasks = [
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Task(id=0, name='label_classification', type='seq_classification', num_labels=5),
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