Instructions to use AnuradhaPoddar/seq_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AnuradhaPoddar/seq_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AnuradhaPoddar/seq_classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("AnuradhaPoddar/seq_classification") model = AutoModelForSequenceClassification.from_pretrained("AnuradhaPoddar/seq_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from AnuradhaPoddar/seq_classification: direct link, hf CLI and curl.
- Browser
- Download file 4.92 kB
-
https://huggingface.co/AnuradhaPoddar/seq_classification/resolve/main/training_args.bin
- Command line
-
hf download hf://AnuradhaPoddar/seq_classification/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/AnuradhaPoddar/seq_classification/resolve/main/training_args.bin
4.92 kB
- Xet hash:
- dc7d0988d3592be43cfc53be101e35f732259aa3b0a71c979c52f33c4bdedbfd
- Size of remote file:
- 4.92 kB
- SHA256:
- cfcaba1e3b5f45ab4efe552137e89d7e563ff93df6b127d640c0d94248ebc92f
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