Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use dgalik/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dgalik/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dgalik/finetuning-sentiment-model-3000-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dgalik/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("dgalik/finetuning-sentiment-model-3000-samples") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7960e63cba0b826a1c351d9c0bf606fa8ade25f8631020ac10bbe3f8da7f1729
- Size of remote file:
- 268 MB
- SHA256:
- f61238473fc97ef12d46d40a9cbacf2db3e7b9477b057b5df86df737e71f00c9
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