Instructions to use palakagl/bert_MultiClass_TextClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use palakagl/bert_MultiClass_TextClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="palakagl/bert_MultiClass_TextClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("palakagl/bert_MultiClass_TextClassification") model = AutoModelForSequenceClassification.from_pretrained("palakagl/bert_MultiClass_TextClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 81681331e74e8123142ec3bcb64d2b9e6124fcbc86c6a7a68609575c484fad0c
- Size of remote file:
- 438 MB
- SHA256:
- cfb5a778673484e3052cd68ad79c774b788a41b62635aea78e5c92ab4435f415
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