Text Classification
Transformers
Safetensors
PyTorch
English
roberta
emotion
text-embeddings-inference
Instructions to use Dimi-G/roberta-base-emotion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Dimi-G/roberta-base-emotion with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Dimi-G/roberta-base-emotion")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Dimi-G/roberta-base-emotion") model = AutoModelForSequenceClassification.from_pretrained("Dimi-G/roberta-base-emotion", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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## Training Details
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The model was trained on a randomized subset of the [dar-ai/emotion](dair-ai/emotion) dataset from the Hugging Face datasets library. Here are the training parameters:
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- **Batch size**: 64
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- **Number of epochs**: 10
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## Training Details
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The model was trained on a randomized subset of the [dar-ai/emotion](https://huggingface.co/datasets/dair-ai/emotion) dataset from the Hugging Face datasets library. Here are the training parameters:
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- **Batch size**: 64
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- **Number of epochs**: 10
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