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")# pip install -U transformers accelerate # 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
Download sample_input.pkl from palakagl/bert_MultiClass_TextClassification: direct link, hf CLI and curl.
- Browser
- Download file 2.85 kB
-
https://huggingface.co/palakagl/bert_MultiClass_TextClassification/resolve/refs%2Fpr%2F1/sample_input.pkl
- Command line
-
hf download hf://palakagl/bert_MultiClass_TextClassification@refs/pr/1/sample_input.pkl
-
curl -L -o sample_input.pkl https://huggingface.co/palakagl/bert_MultiClass_TextClassification/resolve/refs%2Fpr%2F1/sample_input.pkl
2.85 kB
- Xet hash:
- 9abb5fa36215a9a83840a94d261331aa139fe2ed7b706c42b146cd86a4cb89c9
- Size of remote file:
- 2.85 kB
- SHA256:
- 80dcf200897838b3ba1dfb451001bdb16889bb396e2292c7c33bebf0a9352e4c
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