Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use Sahara/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sahara/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Sahara/finetuning-sentiment-model-3000-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Sahara/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("Sahara/finetuning-sentiment-model-3000-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0d28968dfc67a56545d398de327993d0e787d4f4acac532110b5fe9fe9ab1fc0
- Size of remote file:
- 268 MB
- SHA256:
- 5dbdba4632ded87a5e9848eff2b34f500b946ad274534c9b6e45443bc2dd7eb8
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