Text Classification
Transformers
PyTorch
English
bert
sentiment classification
sentiment analysis
text-embeddings-inference
Instructions to use himanshubeniwal/bert_lf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use himanshubeniwal/bert_lf with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="himanshubeniwal/bert_lf")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("himanshubeniwal/bert_lf") model = AutoModelForSequenceClassification.from_pretrained("himanshubeniwal/bert_lf", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from himanshubeniwal/bert_lf: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/himanshubeniwal/bert_lf/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://himanshubeniwal/bert_lf/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/himanshubeniwal/bert_lf/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 75d26bb045fff3dfe0bac76bf4e27d4559ad3f94be3a8b362f88a0a0fa63b019
- Size of remote file:
- 438 MB
- SHA256:
- b5e8da8906c8ec59314faa8920a74f5cecc01fb1ee611f5dc7a6253b3839c2de
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