Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use rahuldebdas79/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rahuldebdas79/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rahuldebdas79/finetuning-sentiment-model-3000-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rahuldebdas79/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("rahuldebdas79/finetuning-sentiment-model-3000-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8574c5a85b4fb276dd48ad3c94d1bb632a91669ef125307f66a541a026c11158
- Size of remote file:
- 3.31 kB
- SHA256:
- c9d8787a110648379d6ff10d859bc166d3337e779105e196be70cdd1bc67535c
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