Instructions to use rasoultilburg/ssc_bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rasoultilburg/ssc_bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rasoultilburg/ssc_bert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rasoultilburg/ssc_bert") model = AutoModelForSequenceClassification.from_pretrained("rasoultilburg/ssc_bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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language: en
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license: gpl-3.0
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widget:
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example_title: "Causal Example 1"
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- text: "Our findings thus far show that the sanction reduced the number of chips that participants allocated to themselves and that it only increased the number of chips allocated to the yellow pool when there were two options."
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example_title: "Causal Example 2"
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language: en
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license: gpl-3.0
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example_title: "Causal Example 1"
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- text: "Our findings thus far show that the sanction reduced the number of chips that participants allocated to themselves and that it only increased the number of chips allocated to the yellow pool when there were two options."
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example_title: "Causal Example 2"
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