Instructions to use srmishra/ce-MiniLM-L6layer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use srmishra/ce-MiniLM-L6layer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="srmishra/ce-MiniLM-L6layer")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("srmishra/ce-MiniLM-L6layer") model = AutoModelForSequenceClassification.from_pretrained("srmishra/ce-MiniLM-L6layer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from srmishra/ce-MiniLM-L6layer: direct link, hf CLI and curl.
- Browser
- Download file 4.66 kB
-
https://huggingface.co/srmishra/ce-MiniLM-L6layer/resolve/main/training_args.bin
- Command line
-
hf download hf://srmishra/ce-MiniLM-L6layer/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/srmishra/ce-MiniLM-L6layer/resolve/main/training_args.bin
4.66 kB
- Xet hash:
- 8adc56d9451f5ca751d74d2d86e5b38db052333dd0cdf54b1900d4660d1fc890
- Size of remote file:
- 4.66 kB
- SHA256:
- c70a50dd1fba84a18188996e7082fbc174c8968e4f723cdd28a675b46bec88b1
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