Instructions to use UVA-MSBA/Mod4_T7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UVA-MSBA/Mod4_T7 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="UVA-MSBA/Mod4_T7")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("UVA-MSBA/Mod4_T7") model = AutoModelForSequenceClassification.from_pretrained("UVA-MSBA/Mod4_T7") - Notebooks
- Google Colab
- Kaggle
Commit ·
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Parent(s): 3d44354
Update README.md
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README.md
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@@ -16,8 +16,8 @@ from transformers import AutoModelForSequenceClassification
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from transformers import TFAutoModelForSequenceClassification
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from transformers import AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained("
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model = AutoModelForSequenceClassification.from_pretrained("
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def adr_predict(x):
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encoded_input = tokenizer(x, return_tensors='pt')
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from transformers import TFAutoModelForSequenceClassification
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from transformers import AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained("UVA-MSBA/Mod4_T7")
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model = AutoModelForSequenceClassification.from_pretrained("UVA-MSBA/Mod4_T7")
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def adr_predict(x):
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encoded_input = tokenizer(x, return_tensors='pt')
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