Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use RRajesh27/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RRajesh27/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="RRajesh27/finetuning-sentiment-model-3000-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("RRajesh27/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("RRajesh27/finetuning-sentiment-model-3000-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3acb18a56ef84654e8107cb9f7d289c2f3eaec90355574f31bbc90d0a0843ec3
- Size of remote file:
- 268 MB
- SHA256:
- ea2f053baf7e974c3deb1dc2fada461e8ab4c0872f0af7aa5d1139aa600ea109
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