Instructions to use RecordedFuture/Swedish-Sentiment-Fear-Targets with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RecordedFuture/Swedish-Sentiment-Fear-Targets with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="RecordedFuture/Swedish-Sentiment-Fear-Targets")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("RecordedFuture/Swedish-Sentiment-Fear-Targets") model = AutoModelForTokenClassification.from_pretrained("RecordedFuture/Swedish-Sentiment-Fear-Targets", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from RecordedFuture/Swedish-Sentiment-Fear-Targets: direct link, hf CLI and curl.
- Browser
- Download file 496 MB
-
https://huggingface.co/RecordedFuture/Swedish-Sentiment-Fear-Targets/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://RecordedFuture/Swedish-Sentiment-Fear-Targets/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/RecordedFuture/Swedish-Sentiment-Fear-Targets/resolve/main/flax_model.msgpack
496 MB
- Xet hash:
- fe8ea9804ce644c8b79354ad366a42aa36be49fe3a11a2860a3eb1314b059950
- Size of remote file:
- 496 MB
- SHA256:
- cf2f3d0215a526e5552f7423ed7e232e4937e69658f54182c3fa3de0f3ca9d01
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