Instructions to use DunnBC22/fnet-base-Financial_Sentiment_Analysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DunnBC22/fnet-base-Financial_Sentiment_Analysis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="DunnBC22/fnet-base-Financial_Sentiment_Analysis")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("DunnBC22/fnet-base-Financial_Sentiment_Analysis") model = AutoModelForSequenceClassification.from_pretrained("DunnBC22/fnet-base-Financial_Sentiment_Analysis") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
#1
by SFconvertbot - opened
- model.safetensors +3 -0
model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:0e1216444ce993233bec9df943dde74f3629fe87e948ed17bf04e74dfa2a2ac9
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size 331469884
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