Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use Jherb/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jherb/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jherb/finetuning-sentiment-model-3000-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jherb/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("Jherb/finetuning-sentiment-model-3000-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7dd119893d37fa815e4f7e765f8fc397403b162834c496e3a1ce79a45f669c81
- Size of remote file:
- 268 MB
- SHA256:
- b9eda48b74e6dc8ffb5a4ff9ec622577ec6cc510b32ade60d8e1596ec126ea6a
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