Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use kiatkock/sentiment_pc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kiatkock/sentiment_pc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="kiatkock/sentiment_pc")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("kiatkock/sentiment_pc") model = AutoModelForSequenceClassification.from_pretrained("kiatkock/sentiment_pc", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from kiatkock/sentiment_pc: direct link, hf CLI and curl.
- Browser
- Download file 5.11 kB
-
https://huggingface.co/kiatkock/sentiment_pc/resolve/main/training_args.bin
- Command line
-
hf download hf://kiatkock/sentiment_pc/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/kiatkock/sentiment_pc/resolve/main/training_args.bin
5.11 kB
- Xet hash:
- 6002c939015e47748ec0e5995cc3133419ac09bfddecf86a9ae79eaef5f1c748
- Size of remote file:
- 5.11 kB
- SHA256:
- 74bf7a8f16fafc9c219f49019c4f4f3c3daa20bce763193e724c1b1af99b4473
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.