Instructions to use HARSHU550/Bert_IF_Classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HARSHU550/Bert_IF_Classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="HARSHU550/Bert_IF_Classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("HARSHU550/Bert_IF_Classifier") model = AutoModelForSequenceClassification.from_pretrained("HARSHU550/Bert_IF_Classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from HARSHU550/Bert_IF_Classifier: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/HARSHU550/Bert_IF_Classifier/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://HARSHU550/Bert_IF_Classifier/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/HARSHU550/Bert_IF_Classifier/resolve/main/flax_model.msgpack
438 MB
- Xet hash:
- 24b6858c2872c473a091d56bc2522d4fe53291dcefb2f18963df932e99acfe9b
- Size of remote file:
- 438 MB
- SHA256:
- 8d864a49900962d8f95ce6df87e68fd35dd72f31d92f54ce823d06e1ebd24bfd
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