Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use HCKLab/BiBert-MultiTask-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HCKLab/BiBert-MultiTask-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="HCKLab/BiBert-MultiTask-2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("HCKLab/BiBert-MultiTask-2") model = AutoModelForSequenceClassification.from_pretrained("HCKLab/BiBert-MultiTask-2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
add custom handler and modify pipeline
Browse files
__pycache__/bert_for_sequence_classification.cpython-37.pyc
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__pycache__/bibert_multitask_classification.cpython-37.pyc
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__pycache__/handler.cpython-37.pyc
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handler.py
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@@ -46,9 +46,13 @@ class EndpointHandler():
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Return:
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A :obj:`list` | `dict`: will be serialized and returned
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"""
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inputs = data.pop("inputs", data)
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lang = data.pop("lang", None)
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prediction_p = self.classifier_p(inputs)
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label = prediction_p[0]['label']
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Return:
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A :obj:`list` | `dict`: will be serialized and returned
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"""
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inputs = data.pop("inputs", data)
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lang = data.pop("lang", None)
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logger.info(inputs)
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logger.info(lang)
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if isinstance(inputs, str):
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inputs = [inputs]
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prediction_p = self.classifier_p(inputs)
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label = prediction_p[0]['label']
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