Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use varunsappa/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use varunsappa/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="varunsappa/finetuning-sentiment-model-3000-samples")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("varunsappa/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("varunsappa/finetuning-sentiment-model-3000-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from varunsappa/finetuning-sentiment-model-3000-samples: direct link, hf CLI and curl.
- Browser
- Download file 3.44 kB
-
https://huggingface.co/varunsappa/finetuning-sentiment-model-3000-samples/resolve/main/training_args.bin
- Command line
-
hf download hf://varunsappa/finetuning-sentiment-model-3000-samples/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/varunsappa/finetuning-sentiment-model-3000-samples/resolve/main/training_args.bin
3.44 kB
- Xet hash:
- cd4adc62e7e768ae7043f9f74779cf092441a78440b0daa4febe451d1658ae28
- Size of remote file:
- 3.44 kB
- SHA256:
- e86abe2857ca714a43053fc55397877da471544157dbd8f5d8e5615f8de3f9e2
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