Text Classification
Transformers
PyTorch
deberta-v2
Generated from Trainer
text-embeddings-inference
Instructions to use Elron/deberta-v3-large-sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Elron/deberta-v3-large-sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Elron/deberta-v3-large-sentiment")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Elron/deberta-v3-large-sentiment") model = AutoModelForSequenceClassification.from_pretrained("Elron/deberta-v3-large-sentiment", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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# deberta-v3-large-sentiment-lr5e-6-gas2-ls0.0
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This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on an
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It achieves the following results on the evaluation set:
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- Loss: 1.3253
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- Accuracy: 0.7365
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# deberta-v3-large-sentiment-lr5e-6-gas2-ls0.0
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This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on an [tweet_eval/sentiment](https://huggingface.co/microsoft/deberta-v3-large) dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.3253
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- Accuracy: 0.7365
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