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:
- 55c70a0a96ee7b400b7c31b3a6b5024201bfd849d9ecd44b6c361672a186775e
- Size of remote file:
- 3.38 kB
- SHA256:
- 619425ddbe903a70d480e27dd422cc6a19676ed98ccfd92d318d4fa444ab17f8
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