Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use mmeet611/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mmeet611/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mmeet611/finetuning-sentiment-model-3000-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mmeet611/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("mmeet611/finetuning-sentiment-model-3000-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3c3ec36b43d6cd47ce642ee243e2a69a3c871d61e5c742de0321e31a46c39257
- Size of remote file:
- 268 MB
- SHA256:
- c75f0889dce8626fbd5fb8c17fa3e7f6fb7ecbfa2e4af869d89b409df7537013
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