Instructions to use RecordedFuture/Swedish-Sentiment-Fear with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RecordedFuture/Swedish-Sentiment-Fear with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="RecordedFuture/Swedish-Sentiment-Fear")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("RecordedFuture/Swedish-Sentiment-Fear") model = AutoModelForSequenceClassification.from_pretrained("RecordedFuture/Swedish-Sentiment-Fear", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from RecordedFuture/Swedish-Sentiment-Fear: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/RecordedFuture/Swedish-Sentiment-Fear/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://RecordedFuture/Swedish-Sentiment-Fear/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/RecordedFuture/Swedish-Sentiment-Fear/resolve/main/flax_model.msgpack
499 MB
- Xet hash:
- 488274b14332b3d7e443e0c076d10c643e25aca3ba8fab39b80050c05655c840
- Size of remote file:
- 499 MB
- SHA256:
- 7ee9e94d8f164538c9700526430954c01a1797f4eb1e24e8154a5f0d0cc8376e
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