Instructions to use Ramuvannela/Stanford-Sentiment-Treebank with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ramuvannela/Stanford-Sentiment-Treebank with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Ramuvannela/Stanford-Sentiment-Treebank")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Ramuvannela/Stanford-Sentiment-Treebank") model = AutoModelForSequenceClassification.from_pretrained("Ramuvannela/Stanford-Sentiment-Treebank") - Notebooks
- Google Colab
- Kaggle
Stanford-Sentiment-Treebank
This model is a fine-tuned version of gchhablani/bert-base-cased-finetuned-sst2 on an unknown dataset. It achieves the following results on the evaluation set:
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- optimizer: None
- training_precision: float32
Training results
Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
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