Text Classification
Transformers
Safetensors
edlm
feature-extraction
decision-model
system-one
typed-decisions
decision-index
classification
calibrated-probabilities
decisionbench
zero-shot
recommendation
custom_code
Instructions to use nace-ai/drex-dlm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nace-ai/drex-dlm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nace-ai/drex-dlm", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("nace-ai/drex-dlm", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from nace-ai/drex-dlm: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/nace-ai/drex-dlm/resolve/main/tokenizer.json
- Command line
-
hf download hf://nace-ai/drex-dlm/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/nace-ai/drex-dlm/resolve/main/tokenizer.json
11.4 MB
- Xet hash:
- dde3c5a4d468da6fe61f11ffa8f04af2d101f8f4a0fe118c37bd31bb8c3017b6
- Size of remote file:
- 11.4 MB
- SHA256:
- be75606093db2094d7cd20f3c2f385c212750648bd6ea4fb2bf507a6a4c55506
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.