Instructions to use JP040/bert-german-sentiment-twitter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JP040/bert-german-sentiment-twitter with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="JP040/bert-german-sentiment-twitter")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("JP040/bert-german-sentiment-twitter") model = AutoModelForSequenceClassification.from_pretrained("JP040/bert-german-sentiment-twitter", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from JP040/bert-german-sentiment-twitter: direct link, hf CLI and curl.
- Browser
- Download file 436 MB
-
https://huggingface.co/JP040/bert-german-sentiment-twitter/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://JP040/bert-german-sentiment-twitter/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/JP040/bert-german-sentiment-twitter/resolve/main/flax_model.msgpack
436 MB
- Xet hash:
- 6f0561171a3a9b8e645029b412d7fed3362a0db6916f33989d05f688c0903e60
- Size of remote file:
- 436 MB
- SHA256:
- 24338c51accba8d86cc54d65417522a91351707c823c7c67ad0790f94080ca7d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.