Instructions to use vaibhav9/hangman-bert-mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vaibhav9/hangman-bert-mini with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="vaibhav9/hangman-bert-mini")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("vaibhav9/hangman-bert-mini") model = AutoModelForMaskedLM.from_pretrained("vaibhav9/hangman-bert-mini", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
#1
by SFconvertbot - opened
- model.safetensors +3 -0
model.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:7c1ea8f563ec462ee9c1e93ef33938ec80192b410ada8ec58127183d27fe6d33
|
| 3 |
+
size 44814984
|