Text Classification
Transformers
Safetensors
English
bert
customer-complaints
multiclass
huggingface
fine-tuned
wandb
Instructions to use hblim/bert-customer-complaints-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hblim/bert-customer-complaints-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hblim/bert-customer-complaints-classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hblim/bert-customer-complaints-classifier") model = AutoModelForSequenceClassification.from_pretrained("hblim/bert-customer-complaints-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Tuned cosine learning rate
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Tuned batch size and learning rate
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Tuned batch size and learning rate
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initial commit
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