Instructions to use ai-research-lab/bert-question-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ai-research-lab/bert-question-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ai-research-lab/bert-question-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ai-research-lab/bert-question-classifier") model = AutoModelForSequenceClassification.from_pretrained("ai-research-lab/bert-question-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit ·
0d24632
1
Parent(s): 4885918
Upload BertForSequenceClassification
Browse files- config.json +2 -21
config.json
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{
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"_name_or_path": "
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"architectures": [
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"BertForSequenceClassification"
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],
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"attention_probs_dropout_prob": 0.1,
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"classifier_dropout": null,
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"custom_pipelines": {
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"question-classifier": {
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"default": {
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"model": {
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"pt": [
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"ai-research-lab/bert-question-classifier",
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"main"
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]
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}
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},
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"impl": "classifier_pipeline.MultiTaskClassifierPipeline",
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"pt": [
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"AutoModelForSequenceClassification"
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],
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"tf": [],
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"type": "text"
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}
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},
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"gradient_checkpointing": false,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"threshold": 0
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}
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]
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}
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"random": "marimo notebook run 1"
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},
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"torch_dtype": "float32",
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"transformers_version": "4.47.1",
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{
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"_name_or_path": "google-bert/bert-large-uncased",
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"architectures": [
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"BertForSequenceClassification"
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],
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"attention_probs_dropout_prob": 0.1,
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"classifier_dropout": null,
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"gradient_checkpointing": false,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"threshold": 0
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}
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]
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}
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},
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"torch_dtype": "float32",
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"transformers_version": "4.47.1",
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