Instructions to use Minaaaa/deberta_imdb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Minaaaa/deberta_imdb with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Minaaaa/deberta_imdb")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Minaaaa/deberta_imdb") model = AutoModelForSequenceClassification.from_pretrained("Minaaaa/deberta_imdb", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 574 Bytes
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datasets:
- imdb
language:
- en
metrics:
- accuracy
library_name: transformers
---
DeBERTa is a fine-tuned version of DeBERTa model. The dataset used for fine tuning is the IMDB dataset from Hugging Face. The number of instances used for training is 12 500, and 2 500 for testing. Metrics used for evaluation are classification oriented, because the model is used for sentiment analysis. The number of epochs used for training is 5, and the learning rate was set to lr = 5e-5. The results obtained are:
Accuracy: 0.9312
Precision: 0.9268
Recall: 0.9371
F1-score: 0.9319 |