Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use aiscientist/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aiscientist/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="aiscientist/finetuning-sentiment-model-3000-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("aiscientist/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("aiscientist/finetuning-sentiment-model-3000-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 44c4f2c30f290723ca00b39935c0ffa43ec8ed896655acf0072ac1cc1834efaa
- Size of remote file:
- 268 MB
- SHA256:
- 54ff6ec6090ddc967887d9241a88a33453f47de53540ea43221d4a7c43f4a992
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