Instructions to use mgh6/TCS_MLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mgh6/TCS_MLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="mgh6/TCS_MLM")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("mgh6/TCS_MLM") model = AutoModelForMaskedLM.from_pretrained("mgh6/TCS_MLM", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download last-checkpoint/optimizer.pt from mgh6/TCS_MLM: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/mgh6/TCS_MLM/resolve/main/last-checkpoint/optimizer.pt
- Command line
-
hf download hf://mgh6/TCS_MLM/last-checkpoint/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/mgh6/TCS_MLM/resolve/main/last-checkpoint/optimizer.pt
268 MB
- Xet hash:
- 05ae585ac1b9a26d77dbc1d013235f300c28ff6db87b11580310be627b64de84
- Size of remote file:
- 268 MB
- SHA256:
- e1e92f6aa1461f021602ec4129ab6fa7dbab49bf5e6d579fc44c475447dbbd9c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.