Text Classification
Transformers
Safetensors
English
bert
PyTorch
multi-class-classification
text-embeddings-inference
Instructions to use AIPsy/bert-base-client-topic-classification-eng with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AIPsy/bert-base-client-topic-classification-eng with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AIPsy/bert-base-client-topic-classification-eng")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("AIPsy/bert-base-client-topic-classification-eng") model = AutoModelForSequenceClassification.from_pretrained("AIPsy/bert-base-client-topic-classification-eng", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9e5154b2f6d3b9865da9641c3bec1f09f7932ae299a50fb7bf9a7cc5ed44f750
- Size of remote file:
- 438 MB
- SHA256:
- f901ca43d967074618798c1cba0d4d7f060561088e253204f5480d5517dbe09c
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