Instructions to use avichr/heBERT_sentiment_analysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use avichr/heBERT_sentiment_analysis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="avichr/heBERT_sentiment_analysis")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("avichr/heBERT_sentiment_analysis") model = AutoModelForSequenceClassification.from_pretrained("avichr/heBERT_sentiment_analysis", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from avichr/heBERT_sentiment_analysis: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/avichr/heBERT_sentiment_analysis/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://avichr/heBERT_sentiment_analysis/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/avichr/heBERT_sentiment_analysis/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 3b8b8faae2542996dea831c10cedc3bd4259e2124cc88e0771a37309e405bb28
- Size of remote file:
- 438 MB
- SHA256:
- a77af041ae166db845c326be483417c7b9a44391fed8b53c394384d9d6908997
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