Instructions to use rsher60/bert_base_uncased_text-classification-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rsher60/bert_base_uncased_text-classification-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rsher60/bert_base_uncased_text-classification-model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rsher60/bert_base_uncased_text-classification-model") model = AutoModelForSequenceClassification.from_pretrained("rsher60/bert_base_uncased_text-classification-model") - Notebooks
- Google Colab
- Kaggle
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library_name: transformers
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# Model Card for Model ID
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- **Developed by:** [
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- **Funded by [optional]:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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library_name: transformers
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language:
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- en
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base_model:
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- google-bert/bert-base-uncased
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# Model Card for Model ID
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- **Developed by:** [Riddhiman Sherlekar]
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- **Funded by [optional]:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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