Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use mayank-01/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mayank-01/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mayank-01/finetuning-sentiment-model-3000-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mayank-01/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("mayank-01/finetuning-sentiment-model-3000-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 50061a164aaffffb20a68573614f8b2003a57612265284dc298ab3f2886c94b7
- Size of remote file:
- 268 MB
- SHA256:
- bf092e1ed8e7d9b81d5afe2f7af2f53a5c057eb41d831063269d2409fd6c97d1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.