Text Classification
Transformers
PyTorch
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use Sayantan2001/finetuning-sentiment-model-bert-10000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sayantan2001/finetuning-sentiment-model-bert-10000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Sayantan2001/finetuning-sentiment-model-bert-10000-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Sayantan2001/finetuning-sentiment-model-bert-10000-samples") model = AutoModelForSequenceClassification.from_pretrained("Sayantan2001/finetuning-sentiment-model-bert-10000-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 896028c2d99e76a454ddab75160b310342f97d72b0b524a740850129176dadd9
- Size of remote file:
- 4.6 kB
- SHA256:
- 95239b6a81b8a1edac6ce8f9d40fadac1e09b35c41c2c833c174b9949d1a859a
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