Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use brookelove/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use brookelove/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="brookelove/finetuning-sentiment-model-3000-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("brookelove/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("brookelove/finetuning-sentiment-model-3000-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 55c52f411fc067430760d467ef9b7ae0b8de14e7256228c5cbd123ef5cd43509
- Size of remote file:
- 268 MB
- SHA256:
- fa48b8d250cc66bccbdf075ec26603de58ad67ba1404785943d29f61e5b8a33f
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