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 pytorch_model.bin from palakagl/bert_MultiClass_TextClassification: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/palakagl/bert_MultiClass_TextClassification/resolve/refs%2Fpr%2F2/pytorch_model.bin
- Command line
-
hf download hf://palakagl/bert_MultiClass_TextClassification@refs/pr/2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/palakagl/bert_MultiClass_TextClassification/resolve/refs%2Fpr%2F2/pytorch_model.bin
438 MB
- Xet hash:
- eaf754150f287ac37508bb61b4a9ccdaddf2fedb18b429c3f9d3e99de116374e
- Size of remote file:
- 438 MB
- SHA256:
- 38538e3716d9bdead7ba8898434e8bf7dfc072d4059d11c098e66f22906a48a0
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