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:
- e54e0fd60b60e247524b464b4b734b341770ae0f5b7d68963e9750e9dc72af6f
- Size of remote file:
- 3.64 kB
- SHA256:
- 5439fe9ac48aa3376ca32d506f48e32bc67cbfa2974048869f72036f07b1ed36
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