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 flax_model.msgpack 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/flax_model.msgpack
- Command line
-
hf download hf://avichr/heBERT_sentiment_analysis/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/avichr/heBERT_sentiment_analysis/resolve/main/flax_model.msgpack
438 MB
- Xet hash:
- 3530edf5833561ba3e17419365ef5d1cd08e0c84ab6e9e7348e3b1883ad6d8ee
- Size of remote file:
- 438 MB
- SHA256:
- bfe4aaddc08073fd64548b7f15ebfa03783e63f1e8bbfb8c96c144d783f48db2
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