Token Classification
Transformers
Safetensors
qwen2
Generated from Trainer
trl
bidirectional-prm
text-generation-inference
Instructions to use wls04/math_bi7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wls04/math_bi7b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="wls04/math_bi7b")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wls04/math_bi7b") model = AutoModelForTokenClassification.from_pretrained("wls04/math_bi7b", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3da8509dbdfbe8b661f05e293f4d5e91acd1b2f4363e942d02746796db7e7b39
- Size of remote file:
- 11.4 MB
- SHA256:
- e2e97b66530872e0a1cce13dc72aad878c4c979844b8462bf6818d6967fe89f0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.