Instructions to use noahshinn/ts-code2td with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use noahshinn/ts-code2td with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "translation" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("translation", model="noahshinn/ts-code2td", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("noahshinn/ts-code2td", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("noahshinn/ts-code2td", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9b4908c1a5ae7bdea26a0c18963fa3b7089b568c16c82e94f4b54fb2d611002b
- Size of remote file:
- 4.6 GB
- SHA256:
- b46056c04fd91605f925b7a4dc84b666baaaecd9ebae03d0834e0538a51e0856
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.