Instructions to use PavanDeepak/Topic_Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PavanDeepak/Topic_Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="PavanDeepak/Topic_Classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("PavanDeepak/Topic_Classification") model = AutoModelForSequenceClassification.from_pretrained("PavanDeepak/Topic_Classification") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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## Example
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```from transformers import AutoModelForSequenceClassification, TFAutoModelForSequenceClassification```
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```from transformers import AutoTokenizer```
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```import numpy as np```
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```from scipy.special import expit```
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```for i in range(len(predictions)):```
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```if predictions[i]:```
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```print(class_mapping[i])```
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## Output:
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## Example
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```from transformers import AutoModelForSequenceClassification, TFAutoModelForSequenceClassification```
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```from transformers import AutoTokenizer```
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```import numpy as np```
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```from scipy.special import expit```
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```for i in range(len(predictions)):```
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```if predictions[i]:```
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```print(class_mapping[i])```
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## Output:
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