Instructions to use newsmediabias/Bert_Sentiment_Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use newsmediabias/Bert_Sentiment_Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="newsmediabias/Bert_Sentiment_Classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("newsmediabias/Bert_Sentiment_Classification") model = AutoModelForSequenceClassification.from_pretrained("newsmediabias/Bert_Sentiment_Classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from newsmediabias/Bert_Sentiment_Classification: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/newsmediabias/Bert_Sentiment_Classification/resolve/main/model.safetensors
- Command line
-
hf download hf://newsmediabias/Bert_Sentiment_Classification/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/newsmediabias/Bert_Sentiment_Classification/resolve/main/model.safetensors
438 MB
- Xet hash:
- 06abcd14429bc8a23c1b110d2bc76be458b8cddeda5c02e71d888083913fced6
- Size of remote file:
- 438 MB
- SHA256:
- b4bee2bae7675dc719651a0f676dba61d623773b531f9fe043c8c41c43680086
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