Text Classification
Transformers
PyTorch
TensorBoard
opt
Generated from Trainer
Eval Results (legacy)
Instructions to use amosc00/NLP_sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use amosc00/NLP_sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="amosc00/NLP_sentiment")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("amosc00/NLP_sentiment") model = AutoModelForSequenceClassification.from_pretrained("amosc00/NLP_sentiment", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from amosc00/NLP_sentiment: direct link, hf CLI and curl.
- Browser
- Download file 1.32 GB
-
https://huggingface.co/amosc00/NLP_sentiment/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://amosc00/NLP_sentiment/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/amosc00/NLP_sentiment/resolve/main/pytorch_model.bin
1.32 GB
- Xet hash:
- fcf2d2bb2fc7a719ddc35b37adc35d8438b2413bc1e4778de7ddf59da5393199
- Size of remote file:
- 1.32 GB
- SHA256:
- 31458192ff73fbb268124eb8460ce2c54436fe4365a0aeec91879debc8764e93
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