Text Classification
Transformers
Safetensors
Greek
bert
Social Media
Reddit
Topic Classification
Text Classification
Greek NLP
Eval Results (legacy)
text-embeddings-inference
Instructions to use IMISLab/Greek-Reddit-BERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use IMISLab/Greek-Reddit-BERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="IMISLab/Greek-Reddit-BERT")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("IMISLab/Greek-Reddit-BERT") model = AutoModelForSequenceClassification.from_pretrained("IMISLab/Greek-Reddit-BERT") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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@@ -79,7 +79,6 @@ We fine-tuned `nlpaueb/bert-base-greek-uncased-v1` (110 million parameters) on t
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* Learning rate = 5e−5
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* Dropout Rate = 0.1
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* Number of labels = 10
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* No warmup steps
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* 32-bit floating precision
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* Tokenization
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* maximum input token length = 512
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* Learning rate = 5e−5
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* Dropout Rate = 0.1
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* Number of labels = 10
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* 32-bit floating precision
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* Tokenization
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* maximum input token length = 512
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