Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use mayank-01/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mayank-01/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mayank-01/finetuning-sentiment-model-3000-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mayank-01/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("mayank-01/finetuning-sentiment-model-3000-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6ef642591a340c80d654ffca33479085527edebef27881a290d0fd04220102a2
- Size of remote file:
- 3.38 kB
- SHA256:
- dd2800d64c6d01a5a1c2e0f5b30e60117e844c2127208e408b5accdeb9c82140
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