Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use rithwik-db/finetuning-sentiment-model-3000-samples-4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rithwik-db/finetuning-sentiment-model-3000-samples-4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rithwik-db/finetuning-sentiment-model-3000-samples-4")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rithwik-db/finetuning-sentiment-model-3000-samples-4") model = AutoModelForSequenceClassification.from_pretrained("rithwik-db/finetuning-sentiment-model-3000-samples-4") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
#1
by SFconvertbot - opened
- model.safetensors +3 -0
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oid sha256:ad16b30df96bfbff8158f2f64814b8a7c0378e6329d9ebd42df3acb658326f48
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size 267832560
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