Text Classification
Transformers
Safetensors
English
roberta
sentiment-analysis
literary-sentiment
sentiment-arcs
Eval Results (legacy)
text-embeddings-inference
Instructions to use fpianz/sentiment-fiction-seq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fpianz/sentiment-fiction-seq with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="fpianz/sentiment-fiction-seq")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("fpianz/sentiment-fiction-seq") model = AutoModelForSequenceClassification.from_pretrained("fpianz/sentiment-fiction-seq") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2fd70bf7c03ed2ef8cf75460a5cc4bd65561b20161a8e065342e33e8c08040e9
- Size of remote file:
- 1.42 GB
- SHA256:
- be35837bcf11dcf105475277892c4a50be562f82fd96a930918a113fe8f15ad0
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