Instructions to use HooshvareLab/bert-fa-base-uncased-sentiment-deepsentipers-binary with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HooshvareLab/bert-fa-base-uncased-sentiment-deepsentipers-binary with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="HooshvareLab/bert-fa-base-uncased-sentiment-deepsentipers-binary")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("HooshvareLab/bert-fa-base-uncased-sentiment-deepsentipers-binary") model = AutoModelForSequenceClassification.from_pretrained("HooshvareLab/bert-fa-base-uncased-sentiment-deepsentipers-binary", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from HooshvareLab/bert-fa-base-uncased-sentiment-deepsentipers-binary: direct link, hf CLI and curl.
- Browser
- Download file 651 MB
-
https://huggingface.co/HooshvareLab/bert-fa-base-uncased-sentiment-deepsentipers-binary/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://HooshvareLab/bert-fa-base-uncased-sentiment-deepsentipers-binary/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/HooshvareLab/bert-fa-base-uncased-sentiment-deepsentipers-binary/resolve/main/flax_model.msgpack
651 MB
- Xet hash:
- ddf154e87c5e90424ef6b91ab3a6f934007bdf6d37a9c16f703063555a7b3f98
- Size of remote file:
- 651 MB
- SHA256:
- b8b93487edbd1d88e16680092a8ba453b09937aaa8e06f4e37beacad3bd8d4c5
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