Instructions to use DunnBC22/fnet-large-Financial_Sentiment_Analysis_v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DunnBC22/fnet-large-Financial_Sentiment_Analysis_v3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="DunnBC22/fnet-large-Financial_Sentiment_Analysis_v3")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("DunnBC22/fnet-large-Financial_Sentiment_Analysis_v3") model = AutoModelForSequenceClassification.from_pretrained("DunnBC22/fnet-large-Financial_Sentiment_Analysis_v3") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
#2
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:ef033d56ac3138fb401e3e9075f6be34cc0e5d53b386f7cfd72e0ca8f4b41ced
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size 947821764
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