Text Classification
Transformers
TensorBoard
Safetensors
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use MiMe-MeMo/MeMo-BERT-SA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MiMe-MeMo/MeMo-BERT-SA with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MiMe-MeMo/MeMo-BERT-SA")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("MiMe-MeMo/MeMo-BERT-SA") model = AutoModelForSequenceClassification.from_pretrained("MiMe-MeMo/MeMo-BERT-SA") - Notebooks
- Google Colab
- Kaggle
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# MeMo_BERT-SA
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This model is a fine-tuned version of [MiMe-MeMo/MeMo-BERT-03](https://huggingface.co/MiMe-MeMo/MeMo-BERT-03) on an
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It achieves the following results on the evaluation set:
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- Loss: 2.3157
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- F1-score: 0.7821
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# MeMo_BERT-SA
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This model is a fine-tuned version of [MiMe-MeMo/MeMo-BERT-03](https://huggingface.co/MiMe-MeMo/MeMo-BERT-03) on an https://huggingface.co/MiMe-MeMo/MeMo-Dataset-SA dataset.
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It achieves the following results on the evaluation set:
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- Loss: 2.3157
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- F1-score: 0.7821
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