Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use Silicon23/BERTForDetectingDepression-Twitter2015 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Silicon23/BERTForDetectingDepression-Twitter2015 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Silicon23/BERTForDetectingDepression-Twitter2015")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Silicon23/BERTForDetectingDepression-Twitter2015") model = AutoModelForSequenceClassification.from_pretrained("Silicon23/BERTForDetectingDepression-Twitter2015", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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## Training and evaluation data
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## Training procedure
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## Training and evaluation data
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Eval Precision: 0.9206730769230769
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Eval Recall: 0.9364303178484108
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Eval Accuracy: 0.9275629220380601
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## Training procedure
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