Text Classification
Transformers
Safetensors
English
roberta
sentiment-analysis
literary-sentiment
Eval Results (legacy)
text-embeddings-inference
Instructions to use fpianz/sentiment-fiction with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fpianz/sentiment-fiction with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="fpianz/sentiment-fiction")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("fpianz/sentiment-fiction") model = AutoModelForSequenceClassification.from_pretrained("fpianz/sentiment-fiction", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from fpianz/sentiment-fiction: direct link, hf CLI and curl.
- Browser
- Download file 5.37 kB
-
https://huggingface.co/fpianz/sentiment-fiction/resolve/main/training_args.bin
- Command line
-
hf download hf://fpianz/sentiment-fiction/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/fpianz/sentiment-fiction/resolve/main/training_args.bin
5.37 kB
- Xet hash:
- 4bb12e4ce2ff7ffc3e415503ee6da039b435de5aa1027b2dfe19251ea2c5dbf2
- Size of remote file:
- 5.37 kB
- SHA256:
- 6150f7302996fb29fd17bbbf3220cd46a3c21d140faea6e44dbd27ff0c59aad7
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