Text Classification
Transformers
PyTorch
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use mnarasim/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mnarasim/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mnarasim/finetuning-sentiment-model-3000-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mnarasim/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("mnarasim/finetuning-sentiment-model-3000-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d0b377bae60e5448e62c057c91e80ca87988afeeb3a866c5b0d340faa7657b2a
- Size of remote file:
- 268 MB
- SHA256:
- 6c3125e4c7c8c0edde2643463a20a7b7e995df0425061a44cbac53839c893b93
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